Above are papers related to the following paper: [Paper_ID] Paper #49660. 	[Title] Feature Subset Selection as Search with Probabilistic Estimates [Abstract] Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of concepts induced by supervised learning algorithms. We formulate the search for a feature subset as an abstract search problem with probabilistic estimates. Searching a space using an evaluation function that is a random variable requires trading off accuracy of estimates for increased state exploration. We show how recent feature subset selection algorithms in the machine learning literature fit into this search problem as simple hill climbing approaches, and conduct a small experiment using a best-first search technique. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1132418. 	[Title] NONPARAMETRIC SELECTION OF INPUT VARIABLES FOR CONNECTIONIST LEARNING [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1104261. 	[Title] Using Errors to Create Piecewise Learnable Partitions [Abstract] In this paper we describe an algorithm which exploits the error distribution generated by a learning algorithm in order to break up the domain which is being approximated into piecewise learnable partitions. Traditionally, the error distribution has been neglected in favor of a lump error measure such as RMS. By doing this, however, we lose a lot of important information. The error distribution tells us where the algorithm is doing badly, and if there exists a "ridge" of errors, also tells us how to partition the space so that one part of the space will not interfere with the learning of another. The algorithm builds a variable arity k-d tree whose leaves contain the partitions. Using this tree, new points can be predicted using the correct partition by traversing the tree. We instantiate this algorithm using memory based learners and cross-validation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #17798. 	[Title] Using Decision Trees to Improve Case-Based Learning [Abstract] This paper shows that decision trees can be used to improve the performance of case-based learning (CBL) systems. We introduce a performance task for machine learning systems called semi-flexible prediction that lies between the classification task performed by decision tree algorithms and the flexible prediction task performed by conceptual clustering systems. In semi-flexible prediction, learning should improve prediction of a specific set of features known a priori rather than a single known feature (as in classification) or an arbitrary set of features (as in conceptual clustering). We describe one such task from natural language processing and present experiments that compare solutions to the problem using decision trees, CBL, and a hybrid approach that combines the two. In the hybrid approach, decision trees are used to specify the features to be included in k-nearest neighbor case retrieval. Results from the experiments show that the hybrid approach outperforms both the decision tree and case-based approaches as well as two case-based systems that incorporate expert knowledge into their case retrieval algorithms. Results clearly indicate that decision trees can be used to improve the performance of CBL systems and do so without reliance on potentially expensive expert knowledge. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #104840. 	[Title] An Optimized Theory Revision Module [Abstract] Theory revision systems typically use a set of theory-to-theory transformations f k g to hill-climb from a given initial theory to a new theory whose empirical accuracy, over a given set of labeled training instances fc j g, is a local maximum. At the heart of each such process is an "evaluator", which compares the accuracy of the current theory KB with that of each of its "neighbors" f k (KB)g, with the goal of determining which neighbor has the highest accuracy. The obvious "wrapper" evaluator simply evaluates each individual neighbor theory KB k = k (KB) on each instance c j . As it can be very expensive to evaluate a single theory on a single instance, and there can be a great many training instances and a huge number of neighbors, this approach can be prohibitively slow. We present an alternative system which employs a smarter evaluator that quickly computes the accuracy of a transformed theory k (KB) by "looking inside" KB and reasoning about the effects of the k transformation. We compare the performance of with the naive wrapper system on real-world theories obtained from a fielded expert system, and find that runs over 35 times faster than , while attaining the same accuracy. This paper also discusses 's source of power. Keywords: theory revision, efficient algorithm, hill-climbing system Multiple Submissions: We have submited a related version of this paper to AAAI96. fl We gratefully acknowledge the many helpful comments on this report from George Drastal, Chandra Mouleeswaran and Geoff Towell. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1125386. 	[Title] 21 Using n 2 classifier in constructive induction [Abstract] In this paper, we propose a multi-classification approach for constructive induction. The idea of an improvement of classification accuracy is based on iterative modification of input data space. This process is independently repeated for each pair of n classes. Finally, it gives (n 2 n)/2 input data subspaces of attributes dedicated for optimal discrimination of appropriate pairs of classes. We use genetic algorithms as a constructive induction engine. A final classification is obtained by a weighted majority voting rule, according to n 2 - classifier approach. The computational experiment was performed on medical data set. The obtained results point out the advantage of using a multi-classification model (n 2 classifier) in constructive induction in relation to the analogous single-classifier approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #27530. 	[Title] Learning Symbolic Rules Using Artificial Neural Networks [Abstract] A distinct advantage of symbolic learning algorithms over artificial neural networks is that typically the concept representations they form are more easily understood by humans. One approach to understanding the representations formed by neural networks is to extract symbolic rules from trained networks. In this paper we describe and investigate an approach for extracting rules from networks that uses (1) the NofM extraction algorithm, and (2) the network training method of soft weight-sharing. Previously, the NofM algorithm had been successfully applied only to knowledge-based neural networks. Our experiments demonstrate that our extracted rules generalize better than rules learned using the C4.5 system. In addition to being accurate, our extracted rules are also reasonably comprehensible. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1105932. 	[Title] An adaptation of Relief for attribute estimation in regression [Abstract] Heuristic measures for estimating the quality of attributes mostly assume the independence of attributes so in domains with strong dependencies between attributes their performance is poor. Relief and its extension ReliefF are capable of correctly estimating the quality of attributes in classification problems with strong dependencies between attributes. By exploiting local information provided by different contexts they provide a global view. We present the analysis of Reli-efF which lead us to its adaptation to regression (continuous class) problems. The experiments on artificial and real-world data sets show that Re-gressional ReliefF correctly estimates the quality of attributes in various conditions, and can be used for non-myopic learning of the regression trees. Regressional ReliefF and ReliefF provide a unified view on estimating the attribute quality in regression and classification. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46476. 	[Title] A counter example to the stronger version of the binary tree hypothesis [Abstract] The paper describes a counter example to the hypothesis which states that a greedy decision tree generation algorithm that constructs binary decision trees and branches on a single attribute-value pair rather than on all values of the selected attribute will always lead to a tree with fewer leaves for any given training set. We show also that RELIEFF is less myopic than other impurity functions and that it enables the induction algorithm that generates binary decision trees to reconstruct optimal (the smallest) decision trees in more cases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #114189. 	[Title] Searching for dependencies in Bayesian classifiers j A n V n j If the attributes [Abstract] Naive Bayesian classifiers which make independence assumptions perform remarkably well on some data sets but poorly on others. We explore ways to improve the Bayesian classifier by searching for dependencies among attributes. We propose and evaluate two algorithms for detecting dependencies among attributes and show that the backward sequential elimination and joining algorithm provides the most improvement over the naive Bayesian classifier. The domains on which the most improvement occurs are those domains on which the naive Bayesian classifier is significantly less accurate than a decision tree learner. This suggests that the attributes used in some common databases are not independent conditioned on the class and that the violations of the independence assumption that affect the accuracy of the classifier The Bayesian classifier (Duda & Hart, 1973) is a probabilistic method for classification. It can be used to determine the probability that an example j belongs to class C i given values of attributes of an example represented as a set of n nominally-valued attribute-value pairs of the form A 1 = V 1 j : ^ P (A k = V k j jC i ) may be estimated from the training data. To determine the most likely class of a test example, the probability of each class is computed with Equation 1. A classifier created in this manner is sometimes called a simple (Langley, 1993) or naive (Kononenko, 1990) Bayesian classifier. One important evaluation metric for machine learning methods is the predictive accuracy on unseen examples. This is measured by randomly selecting a subset of the examples in a database to use as training examples and reserving the remainder to be used as test examples. In the case of the simple Bayesian classifier, the training examples are used to estimate probabilities and Equation 1.1 is then used can be detected from training data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #277263. 	[Title] Induction of decision trees and Bayesian classification applied to diagnosis of sport injuries [Abstract] Machine learning techniques can be used to extract knowledge from data stored in medical databases. In our application, various machine learning algorithms were used to extract diagnostic knowledge to support the diagnosis of sport injuries. The applied methods include variants of the Assistant algorithm for top-down induction of decision trees, and variants of the Bayesian classifier. The available dataset was insufficent for reliable diagnosis of all sport injuries considered by the system. Consequently, expert-defined diagnostic rules were added and used as pre-classifiers or as generators of additional training instances for injuries with few training examples. Experimental results show that the classification accuracy and the explanation capability of the naive Bayesian classifier with the fuzzy discretization of numerical attributes was superior to other methods and was estimated as the most appro priate for practical use. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1107067. 	[Title] On the Greediness of Feature Selection Algorithms [Abstract] Based on our analysis and experiments using real-world datasets, we find that the greediness of forward feature selection algorithms does not severely corrupt the accuracy of function approximation using the selected input features, but improves the efficiency significantly. Hence, we propose three greedier algorithms in order to further enhance the efficiency of the feature selection processing. We provide empirical results for linear regression, locally weighted regression and k-nearest-neighbor models. We also propose to use these algorithms to develop an offline Chinese and Japanese handwriting recognition system with auto matically configured, local models. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153169. 	[Title] Automated model selection [Abstract] Many algorithms have parameters that should be set by the user. For most machine learning algorithms parameter setting is a non-trivial task that influence knowledge model returned by the algorithm. Parameter values are usually set approximately according to some characteristics of the target problem, obtained in different ways. The usual way is to use background knowledge about the target problem (if any) and perform some testing experiments. The paper presents an approach to automated model selection based on local optimization that uses an empirical evaluation of the constructed concept description to guide the search. The approach was tested by using the inductive concept learning system Magnus Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #552469. 	[Title] On Learning More Concepts [Abstract] The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size. This paper asks whether good learning algorithms can be designed by maximizing their coverage. The paper extends a previous upper bound on the coverage of any Boolean concept learning algorithm and describes two algorithms|Multi-Balls and Large-Ball|whose coverage approaches this upper bound. Experimental measurement of the coverage of the ID3 and FRINGE algorithms shows that their coverage is far below this bound. Further analysis of Large-Ball shows that although it learns many concepts, these do not seem to be very interesting concepts. Hence, coverage maximization alone does not appear to yield practically-useful learning algorithms. The paper concludes with a definition of coverage within a bias, which suggests a way that coverage maximization could be applied to strengthen weak preference biases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #158614. 	[Title] Efficient Algorithms for Identifying Relevant Features [Abstract] This paper describes efficient methods for exact and approximate implementation of the MIN-FEATURES bias, which prefers consistent hypotheses definable over as few features as possible. This bias is useful for learning domains where many irrelevant features are present in the training data. We first introduce FOCUS-2, a new algorithm that exactly implements the MIN-FEATURES bias. This algorithm is empirically shown to be substantially faster than the FOCUS algorithm previously given in [ Al-muallim and Dietterich, 1991 ] . We then introduce the Mutual-Information-Greedy, Simple-Greedy and Weighted-Greedy algorithms, which apply efficient heuristics for approximating the MIN-FEATURES bias. These algorithms employ greedy heuristics that trade optimality for computational efficiency. Experimental studies show that the learning performance of ID3 is greatly improved when these algorithms are used to preprocess the training data by eliminating the irrelevant features from ID3's consideration. In particular, the Weighted-Greedy algorithm provides an excellent and efficient approximation of the MIN Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #964248. 	[Title] Issues in the Integration of Data Mining and Data Visualization Visualizing the Simple Bayesian Classifier [Abstract] The simple Bayesian classifier (SBC), sometimes called Naive-Bayes, is built based on a conditional independence model of each attribute given the class. The model was previously shown to be surprisingly robust to obvious violations of this independence assumption, yielding accurate classification models even when there are clear conditional dependencies. The SBC can serve as an excellent tool for initial exploratory data analysis when coupled with a visualizer that makes its structure comprehensible. We describe such a visual representation of the SBC model that has been successfully implemented. We describe the requirements we had for such a visualization and the design decisions we made to satisfy them. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106330. 	[Title] Induction of Condensed Determinations [Abstract] In this paper we suggest determinations as a representation of knowledge that should be easy to understand. We briefly review determinations, which can be displayed in a tabular format, and their use in prediction, which involves a simple matching process. We describe ConDet, an algorithm that uses feature selection to construct determinations from training data, augmented by a condensation process that collapses rows to produce simpler structures. We report experiments that show condensation reduces complexity with no loss of accuracy, then discuss ConDet's relation to other work and outline directions for future studies. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1123867. 	[Title] A Study of Maximal-Coverage Learning Algorithms [Abstract] The coverage of a learning algorithm is the number of concepts that can be learned by that algorithm from samples of a given size. This paper asks whether good learning algorithms can be designed by maximizing their coverage. The paper extends a previous upper bound on the coverage of any Boolean concept learning algorithm and describes two algorithms|Multi-Balls and Large-Ball|whose coverage approaches this upper bound. Experimental measurement of the coverage of the ID3 and FRINGE algorithms shows that their coverage is far below this bound. Further analysis of Large-Ball shows that although it learns many concepts, these do not seem to be very interesting concepts. Hence, coverage maximization alone does not appear to yield practically-useful learning algorithms. The paper concludes with a definition of coverage within a bias, which suggests a way that coverage maximization could be applied to strengthen weak preference biases. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3231. 	[Title] Irrelevant Features and the Subset Selection Problem [Abstract] We address the problem of finding a subset of features that allows a supervised induction algorithm to induce small high-accuracy concepts. We examine notions of relevance and irrelevance, and show that the definitions used in the machine learning literature do not adequately partition the features into useful categories of relevance. We present definitions for irrelevance and for two degrees of relevance. These definitions improve our understanding of the behavior of previous subset selection algorithms, and help define the subset of features that should be sought. The features selected should depend not only on the features and the target concept, but also on the induction algorithm. We describe a method for feature subset selection using cross-validation that is applicable to any induction algorithm, and discuss experiments conducted with ID3 and C4.5 on artificial and real datasets. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #200480. 	[Title] CBET: a Case Base Exploration Tool [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3233. 	[Title] A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection [Abstract] We review accuracy estimation methods and compare the two most common methods: cross-validation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leave-one-out cross-validation. We report on a large-scale experiment|over half a million runs of C4.5 and a Naive-Bayes algorithm|to estimate the effects of different parameters on these algorithms on real-world datasets. For cross-validation, we vary the number of folds and whether the folds are stratified or not; for bootstrap, we vary the number of bootstrap samples. Our results indicate that for real-word datasets similar to ours, the best method to use for model selection is ten-fold stratified cross validation, even if computation power allows using more folds. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #66594. 	[Title] Automatic Parameter Selection by Minimizing Estimated Error [Abstract] We address the problem of finding the parameter settings that will result in optimal performance of a given learning algorithm using a particular dataset as training data. We describe a "wrapper" method, considering determination of the best parameters as a discrete function optimization problem. The method uses best-first search and cross-validation to wrap around the basic induction algorithm: the search explores the space of parameter values, running the basic algorithm many times on training and holdout sets produced by cross-validation to get an estimate of the expected error of each parameter setting. Thus, the final selected parameter settings are tuned for the specific induction algorithm and dataset being studied. We report experiments with this method on 33 datasets selected from the UCI and StatLog collections using C4.5 as the basic induction algorithm. At a 90% confidence level, our method improves the performance of C4.5 on nine domains, degrades performance on one, and is statistically indistinguishable from C4.5 on the rest. On the sample of datasets used for comparison, our method yields an average 13% relative decrease in error rate. We expect to see similar performance improvements when using our method with other machine learning al gorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #288. 	[Title] Memory Based Stochastic Optimization for Validation and Tuning of Function Approximators [Abstract] This paper focuses on the optimization of hyper-parameters for function approximators. We describe a kind of racing algorithm for continuous optimization problems that spends less time evaluating poor parameter settings and more time honing its estimates in the most promising regions of the parameter space. The algorithm is able to automatically optimize the parameters of a function approximator with less computation time. We demonstrate the algorithm on the problem of finding good parameters for a memory based learner and show the tradeoffs involved in choosing the right amount of computation to spend on each evaluation. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3236. 	[Title] MLC A Machine Learning Library in C [Abstract] We present MLC ++ , a library of C ++ classes and tools for supervised Machine Learning. While MLC ++ provides general learning algorithms that can be used by end users, the main objective is to provide researchers and experts with a wide variety of tools that can accelerate algorithm development, increase software reliability, provide comparison tools, and display information visually. More than just a collection of existing algorithms, MLC ++ is an attempt to extract commonalities of algorithms and decompose them for a unified view that is simple, coherent, and extensible. In this paper we discuss the problems MLC ++ aims to solve, the design of MLC ++ , and the current functionality. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3237. 	[Title] Feature Subset Selection Using the Wrapper Method: Overfitting and Dynamic Search Space Topology [Abstract] In the wrapper approach to feature subset selection, a search for an optimal set of features is made using the induction algorithm as a black box. The estimated future performance of the algorithm is the heuristic guiding the search. Statistical methods for feature subset selection including forward selection, backward elimination, and their stepwise variants can be viewed as simple hill-climbing techniques in the space of feature subsets. We utilize best-first search to find a good feature subset and discuss overfitting problems that may be associated with searching too many feature subsets. We introduce compound operators that dynamically change the topology of the search space to better utilize the information available from the evaluation of feature subsets. We show that compound operators unify previous approaches that deal with relevant and irrelevant features. The improved feature subset selection yields significant improvements for real-world datasets when using the ID3 and the Naive-Bayes induction algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #211875. 	[Title] Protein Structure Prediction: Selecting Salient Features from Large Candidate Pools [Abstract] We introduce a parallel approach, "DT-Select," for selecting features used by inductive learning algorithms to predict protein secondary structure. DT-Select is able to rapidly choose small, nonredundant feature sets from pools containing hundreds of thousands of potentially useful features. It does this by building a decision tree, using features from the pool, that classifies a set of training examples. The features included in the tree provide a compact description of the training data and are thus suitable for use as inputs to other inductive learning algorithms. Empirical experiments in the protein secondary-structure task, in which sets of complex features chosen by DT-Select are used to augment a standard artificial neural network representation, yield surprisingly little performance gain, even though features are selected from very large feature pools. We discuss some possible reasons for this result. 1 Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1153703. 	[Title]  [Abstract] Instance-based learning methods explicitly remember all the data that they receive. They usually have no training phase, and only at prediction time do they perform computation. Then, they take a query, search the database for similar datapoints and build an on-line local model (such as a local average or local regression) with which to predict an output value. In this paper we review the advantages of instance based methods for autonomous systems, but we also note the ensuing cost: hopelessly slow computation as the database grows large. We present and evaluate a new way of structuring a database and a new algorithm for accessing it that maintains the advantages of instance-based learning. Earlier attempts to combat the cost of instance-based learning have sacrificed the explicit retention of all data, or been applicable only to instance-based predictions based on a small number of near neighbors or have had to re-introduce an explicit training phase in the form of an interpolative data structure. Our approach builds a multiresolution data structure to summarize the database of experiences at all resolutions of interest simultaneously. This permits us to query the database with the same exibility as a conventional linear search, but at greatly reduced computational cost. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #3232. 	[Title] The Power of Decision Tables [Abstract] We evaluate the power of decision tables as a hypothesis space for supervised learning algorithms. Decision tables are one of the simplest hypothesis spaces possible, and usually they are easy to understand. Experimental results show that on artificial and real-world domains containing only discrete features, IDTM, an algorithm inducing decision tables, can sometimes outperform state-of-the-art algorithms such as C4.5. Surprisingly, performance is quite good on some datasets with continuous features, indicating that many datasets used in machine learning either do not require these features, or that these features have few values. We also describe an incremental method for performing cross-validation that is applicable to incremental learning algorithms including IDTM. Using incremental cross-validation, it is possible to cross-validate a given dataset and IDTM in time that is linear in the number of instances, the number of features, and the number of label values. The time for incremental cross-validation is independent of the number of folds chosen, hence leave-one-out cross-validation and ten-fold cross-validation take the same time. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102761. 	[Title] Knowledge Discovery in International Conflict Databases [Abstract] Artificial Intelligence is heavily supported by military institutions, while practically no effort goes into the investigation of possible contributions of AI to the avoidance and termination of crises and wars. This paper makes a first step into this direction by investigating the use of machine learning techniques for discovering knowledge in international conflict and conflict management databases. We have applied similarity-based case retrieval to the KOSIMO database of international conflicts. Furthermore, we present results of analyzing the CONFMAN database of successful and unsuccessful conflict management attempts with an inductive decision tree learning algorithm. The latter approach seems to be particularly promising, as conflict management events apparently are more repetitive and thus better suited for machine-aided analysis. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #711598. 	[Title] Pattern Theoretic Knowledge Discovery [Abstract] Future research directions in Knowledge Discovery in Databases (KDD) include the ability to extract an overlying concept relating useful data. Current limitations involve the search complexity to find that concept and what it means to be "useful." The Pattern Theory research crosses over in a natural way to the aforementioned domain. The goal of this paper is threefold. First, we present a new approach to the problem of learning by Discovery and robust pattern finding. Second, we explore the current limitations of a Pattern Theoretic approach as applied to the general KDD problem. Third, we exhibit its performance with experimental results on binary functions, and we compare those results with C4.5. This new approach to learning demonstrates a powerful method for finding patterns in a robust manner. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #180399. 	[Title] An Inductive Learning Approach to Prognostic Prediction [Abstract] This paper introduces the Recurrence Surface Approximation, an inductive learning method based on linear programming that predicts recurrence times using censored training examples, that is, examples in which the available training output may be only a lower bound on the "right answer." This approach is augmented with a feature selection method that chooses an appropriate feature set within the context of the linear programming generalizer. Computational results in the field of breast cancer prognosis are shown. A straightforward translation of the prediction method to an artificial neural network model is also proposed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #116528. 	[Title] Selection of Relevant Features and Examples in Machine Learning [Abstract] In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use to compare different methods. We close with some challenges for future work in this area. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128881. 	[Title] Prognosing the femoral neck fracture recovery with machine learning [Abstract] We compare the performance and explanation abilities of several machine learning algorithms in the problem of predicting the femoral neck fracture recovery. Among different algorithms, the semi naive Bayesian classifier and Assistant-R seem to be the most appropriate. We analyze the combination of decisions of several classifiers for solving the prediction problem and show that the combined classifier improves both the performance and explanation ability. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #328370. 	[Title] Expectation-Based Selective Attention for Visual Monitoring and Control of a Robot Vehicle [Abstract] Reliable vision-based control of an autonomous vehicle requires the ability to focus attention on the important features in an input scene. Previous work with an autonomous lane following system, ALVINN [Pomerleau, 1993], has yielded good results in uncluttered conditions. This paper presents an artificial neural network based learning approach for handling difficult scenes which will confuse the ALVINN system. This work presents a mechanism for achieving task-specific focus of attention by exploiting temporal coherence. A saliency map, which is based upon a computed expectation of the contents of the inputs in the next time step, indicates which regions of the input retina are important for performing the task. The saliency map can be used to accentuate the features which are important for the task, and de-emphasize those which are not. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #2354. 	[Title] A Statistical Approach to Solving the EBL Utility Problem [Abstract] Many "learning from experience" systems use information extracted from problem solving experiences to modify a performance element PE, forming a new element PE 0 that can solve these and similar problems more efficiently. However, as transformations that improve performance on one set of problems can degrade performance on other sets, the new PE 0 is not always better than the original PE; this depends on the distribution of problems. We therefore seek the performance element whose expected performance, over this distribution, is optimal. Unfortunately, the actual distribution, which is needed to determine which element is optimal, is usually not known. Moreover, the task of finding the optimal element, even knowing the distribution, is intractable for most interesting spaces of elements. This paper presents a method, palo, that side-steps these problems by using a set of samples to estimate the unknown distribution, and by using a set of transformations to hill-climb to a local optimum. This process is based on a mathematically rigorous form of utility analysis: in particular, it uses statistical techniques to determine whether the result of a proposed transformation will be better than the original system. We also present an efficient way of implementing this learning system in the context of a general class of performance elements, and include empirical evidence that this approach can work effectively. fl Much of this work was performed at the University of Toronto, where it was supported by the Institute for Robotics and Intelligent Systems and by an operating grant from the National Science and Engineering Research Council of Canada. We also gratefully acknowledge receiving many helpful comments from William Cohen, Dave Mitchell, Dale Schuurmans and the anonymous referees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152307. 	[Title]  [Abstract] Instance-based learning methods explicitly remember all the data that they receive. They usually have no training phase, and only at prediction time do they perform computation. Then, they take a query, search the database for similar datapoints and build an on-line local model (such as a local average or local regression) with which to predict an output value. In this paper we review the advantages of instance based methods for autonomous systems, but we also note the ensuing cost: hopelessly slow computation as the database grows large. We present and evaluate a new way of structuring a database and a new algorithm for accessing it that maintains the advantages of instance-based learning. Earlier attempts to combat the cost of instance-based learning have sacrificed the explicit retention of all data, or been applicable only to instance-based predictions based on a small number of near neighbors or have had to re-introduce an explicit training phase in the form of an interpolative data structure. Our approach builds a multiresolution data structure to summarize the database of experiences at all resolutions of interest simultaneously. This permits us to query the database with the same exibility as a conventional linear search, but at greatly reduced computational cost. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #259126. 	[Title] A Monotonic Measure for Optimal Feature Selection [Abstract] Feature selection is a problem of choosing a subset of relevant features. In general, only exhaustive search can bring about the optimal subset. With a monotonic measure, exhaustive search can be avoided without sacrificing optimality. Unfortunately, most error- or distance-based measures are not monotonic. A new measure is employed in this work that is monotonic and fast to compute. The search for relevant features according to this measure is guaranteed to be complete but not exhaustive. Experiments are conducted for verification. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10169. 	[Title] Learning Boolean Concepts in the Presence of Many Irrelevant Features [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #592826. 	[Title] Machine learning in prognosis of the femoral neck fracture recovery examples, estimating attributes, explanation ability, [Abstract] We compare the performance of several machine learning algorithms in the problem of prognos-tics of the femoral neck fracture recovery: the K-nearest neighbours algorithm, the semi-naive Bayesian classifier, backpropagation with weight elimination learning of the multilayered neural networks, the LFC (lookahead feature construction) algorithm, and the Assistant-I and Assistant-R algorithms for top down induction of decision trees using information gain and RELIEFF as search heuristics, respectively. We compare the prognostic accuracy and the explanation ability of different classifiers. Among the different algorithms the semi-naive Bayesian classifier and Assistant-R seem to be the most appropriate. We analyze the combination of decisions of several classifiers for solving prediction problems and show that the combined classifier improves both performance and the explanation ability. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #671293. 	[Title] Selecting Input Variables Using Mutual Information and Nonparametric Density Estimation [Abstract] In learning problems where a connectionist network is trained with a finite sized training set, better generalization performance is often obtained when unneeded weights in the network are eliminated. One source of unneeded weights comes from the inclusion of input variables that provide little information about the output variables. We propose a method for identifying and eliminating these input variables. The method first determines the relationship between input and output variables using nonparametric density estimation and then measures the relevance of input variables using the information theoretic concept of mutual information. We present results from our method on a simple toy problem and a nonlinear time series. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #592830. 	[Title] Context-sensitive attribute estimation in regression [Abstract] One of key issues in both discrete and continuous class prediction and in machine learning in general seems to be the problem of estimating the quality of attributes. Heuristic measures mostly assume independence of attributes so their use is non-optimal in domains with strong dependencies between attributes. For the same reason they are also mostly unable to recognize context dependent features. Relief and its extension Re-liefF are statistical methods capable of correctly estimating the quality of attributes in classification problems with strong dependencies between attributes. By exploiting local information provided by different contexts they provide a global view and recognize contextual attributes. After the analysis of ReliefF we have extended it to continuous class problems. Regressional ReliefF (RReliefF) and ReliefF provide a unified view on estimating attribute quality. The experiments show that RReliefF correctly estimates the quality of attributes, recognizes the contextual attributes and can be used for non myopic learning of the regression trees. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6334. 	[Title] Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms [Abstract] With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the term prototypes refers to the reference instances used in a nearest neighbor computation the instances with respect to which similarity is assessed in order to assign a class to a new data item. Both algorithms rely on stochastic techniques to search the space of sets of prototypes and are simple to implement. The first is a Monte Carlo sampling algorithm; the second applies random mutation hill climbing. On four datasets we show that only three or four prototypes sufficed to give predictive accuracy equal or superior to a basic nearest neighbor algorithm whose run-time storage costs were approximately 10 to 200 times greater. We briefly investigate how random mutation hill climbing may be applied to select features and prototypes simultaneously. Finally, we explain the performance of the sampling algorithm on these datasets in terms of a statistical measure of the extent of clustering displayed by the target classes. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152448. 	[Title]  [Abstract] Instance-based learning methods explicitly remember all the data that they receive. They usually have no training phase, and only at prediction time do they perform computation. Then, they take a query, search the database for similar datapoints and build an on-line local model (such as a local average or local regression) with which to predict an output value. In this paper we review the advantages of instance based methods for autonomous systems, but we also note the ensuing cost: hopelessly slow computation as the database grows large. We present and evaluate a new way of structuring a database and a new algorithm for accessing it that maintains the advantages of instance-based learning. Earlier attempts to combat the cost of instance-based learning have sacrificed the explicit retention of all data, or been applicable only to instance-based predictions based on a small number of near neighbors or have had to re-introduce an explicit training phase in the form of an interpolative data structure. Our approach builds a multiresolution data structure to summarize the database of experiences at all resolutions of interest simultaneously. This permits us to query the database with the same exibility as a conventional linear search, but at greatly reduced computational cost. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10177. 	[Title] Learning in the Presence of Malicious Errors [Abstract] In this paper we study an extension of the distribution-free model of learning introduced by Valiant [23] (also known as the probably approximately correct or PAC model) that allows the presence of malicious errors in the examples given to a learning algorithm. Such errors are generated by an adversary with unbounded computational power and access to the entire history of the learning algorithm's computation. Thus, we study a worst-case model of errors. Our results include general methods for bounding the rate of error tolerable by any learning algorithm, efficient algorithms tolerating nontrivial rates of malicious errors, and equivalences between problems of learning with errors and standard combinatorial optimization problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106370. 	[Title] The Effective Size of a Neural Network: A Principal Component Approach [Abstract] Often when learning from data, one attaches a penalty term to a standard error term in an attempt to prefer simple models and prevent overfitting. Current penalty terms for neural networks, however, often do not take into account weight interaction. This is a critical drawback since the effective number of parameters in a network usually differs dramatically from the total number of possible parameters. In this paper, we present a penalty term that uses Principal Component Analysis to help detect functional redundancy in a neural network. Results show that our new algorithm gives a much more accurate estimate of network complexity than do standard approaches. As a result, our new term should be able to improve techniques that make use of a penalty term, such as weight decay, weight pruning, feature selection, Bayesian, and prediction-risk tech niques. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #636098. 	[Title] NP-Completeness of Searches for Smallest Possible Feature Sets a subset of the set of all [Abstract] In many learning problems, the learning system is presented with values for features that are actually irrelevant to the concept it is trying to learn. The FOCUS algorithm, due to Almuallim and Dietterich, performs an explicit search for the smallest possible input feature set S that permits a consistent mapping from the features in S to the output feature. The FOCUS algorithm can also be seen as an algorithm for learning determinations or functional dependencies, as suggested in [6]. Another algorithm for learning determinations appears in [7]. The FOCUS algorithm has superpolynomial runtime, but Almuallim and Di-etterich leave open the question of tractability of the underlying problem. In this paper, the problem is shown to be NP-complete. We also describe briefly some experiments that demonstrate the benefits of determination learning, and show that finding lowest-cardinality determinations is easier in practice than finding minimal determi Define the MIN-FEATURES problem as follows: given a set X of examples (which are each composed of a a binary value specifying the value of the target feature and a vector of binary values specifying the values of the other features) and a number n, determine whether or not there exists some feature set S such that: We show that MIN-FEATURES is NP-complete by reducing VERTEX-COVER to MIN-FEATURES. 1 The VERTEX-COVER problem may be stated as the question: given a graph G with vertices V and edges E, is there a subset V 0 of V , of size m, such that each edge in E is connected to at least one vertex in V 0 ? We may reduce an instance of VERTEX-COVER to an instance of MIN-FEATURES by mapping each edge in E to an example in X, with one input feature for every vertex in V . 1 In [8], a "proof" is reported for this result by reduction to set covering. The proof therefore fails to show NP-completeness. nations. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #75972. 	[Title] Overcoming the myopia of inductive learning algorithms with RELIEFF [Abstract] Current inductive machine learning algorithms typically use greedy search with limited looka-head. This prevents them to detect significant conditional dependencies between the attributes that describe training objects. Instead of myopic impurity functions and lookahead, we propose to use RELI-EFF, an extension of RELIEF developed by Kira and Rendell [10], [11], for heuristic guidance of inductive learning algorithms. We have reimplemented Assistant, a system for top down induction of decision trees, using RELIEFF as an estimator of attributes at each selection step. The algorithm is tested on several artificial and several real world problems and the results are compared with some other well known machine learning algorithms. Excellent results on artificial data sets and two real world problems show the advantage of the presented approach to inductive learning. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #15429. 	[Title] A General Lower Bound on the Number of Examples Needed for Learning [Abstract] We prove a lower bound of ( 1 * ln 1 ffi + VCdim(C) * ) on the number of random examples required for distribution-free learning of a concept class C, where VCdim(C) is the Vapnik-Chervonenkis dimension and * and ffi are the accuracy and confidence parameters. This improves the previous best lower bound of ( 1 * ln 1 ffi + VCdim(C)), and comes close to the known general upper bound of O( 1 ffi + VCdim(C) * ln 1 * ) for consistent algorithms. We show that for many interesting concept classes, including kCNF and kDNF, our bound is actually tight to within a constant factor. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113926. 	[Title] Improving RBF Networks by the Feature Selection Approach EUBAFES [Abstract] The curse of dimensionality is one of the severest problems concerning the application of RBF networks. The number of RBF nodes and therefore the number of training examples needed grows exponentially with the intrinsic dimensionality of the input space. One way to address this problem is the application of feature selection as a data preprocessing step. In this paper we propose a two-step approach for the determination of an optimal feature subset: First, all possible feature-subsets are reduced to those with best discrimination properties by the application of the fast and robust filter technique EUBAFES. Secondly we use a wrapper approach to judge, which of the pre-selected feature subsets leads to RBF networks with least complexity and best classification accuracy. Experiments are undertaken to show the improvement for RBF networks by our feature selection approach. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #85452. 	[Title] Constructive Induction Using a Non-Greedy Strategy for Feature Selection [Abstract] We present a method for feature construction and selection that finds a minimal set of conjunctive features that are appropriate to perform the classification task. For problems where this bias is appropriate, the method outperforms other constructive induction algorithms and is able to achieve higher classification accuracy. The application of the method in the search for minimal multi-level boolean expressions is presented and analyzed with the help of some examples. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #195150. 	[Title] Naive Bayesian classifier within ILP-R [Abstract] When dealing with the classification problems, current ILP systems often lag behind state-of-the-art attributional learners. Part of the blame can be ascribed to a much larger hypothesis space which, therefore, cannot be as thoroughly explored. However, sometimes it is due to the fact that ILP systems do not take into account the probabilistic aspects of hypotheses when classifying unseen examples. This paper proposes just that. We developed a naive Bayesian classifier within our ILP-R first order learner. The learner itself uses a clever RELIEF based heuristic which is able to detect strong dependencies within the literal space when such dependencies exist. We conducted a series of experiments on artificial and real-world data sets. The results show that the combination of ILP-R together with the naive Bayesian classifier sometimes significantly improves the classification of unseen instances as measured by both classification accuracy and average information score. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1131471. 	[Title] Search-based Class Discretization [Abstract] We present a methodology that enables the use of classification algorithms on regression tasks. We implement this method in system RECLA that transforms a regression problem into a classification one and then uses an existent classification system to solve this new problem. The transformation consists of mapping a continuous variable into an ordinal variable by grouping its values into an appropriate set of intervals. We use misclassification costs as a means to reflect the implicit ordering among the ordinal values of the new variable. We describe a set of alternative discretization methods and, based on our experimental results, justify the need for a search-based approach to choose the best method. Our experimental results confirm the validity of our search-based approach to class discretization, and reveal the accuracy benefits of adding misclassification costs. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1115471. 	[Title] Discovering Compressive Partial Determinations in Mixed Numerical and Symbolic Domains [Abstract] Partial determinations are an interesting form of dependency between attributes in a relation. They generalize functional dependencies by allowing exceptions. We modify a known MDL formula for evaluating such partial determinations to allow for its use in an admissible heuristic in exhaustive search. Furthermore we describe an efficient preprocessing-based approach for handling numerical attributes. An empirical investigation tries to evaluate the viability of the presented ideas. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1152975. 	[Title]  [Abstract] Instance-based learning methods explicitly remember all the data that they receive. They usually have no training phase, and only at prediction time do they perform computation. Then, they take a query, search the database for similar datapoints and build an on-line local model (such as a local average or local regression) with which to predict an output value. In this paper we review the advantages of instance based methods for autonomous systems, but we also note the ensuing cost: hopelessly slow computation as the database grows large. We present and evaluate a new way of structuring a database and a new algorithm for accessing it that maintains the advantages of instance-based learning. Earlier attempts to combat the cost of instance-based learning have sacrificed the explicit retention of all data, or been applicable only to instance-based predictions based on a small number of near neighbors or have had to re-introduce an explicit training phase in the form of an interpolative data structure. Our approach builds a multiresolution data structure to summarize the database of experiences at all resolutions of interest simultaneously. This permits us to query the database with the same exibility as a conventional linear search, but at greatly reduced computational cost. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39124. 	[Title] The Utility of Feature Weighting in Nearest-Neighbor Algorithms [Abstract] Nearest-neighbor algorithms are known to depend heavily on their distance metric. In this paper, we investigate the use of a weighted Euclidean metric in which the weight for each feature comes from a small set of options. We describe Diet, an algorithm that directs search through a space of discrete weights using cross-validation error as its evaluation function. Although a large set of possible weights can reduce the learner's bias, it can also lead to increased variance and overfitting. Our empirical study shows that, for many data sets, there is an advantage to weighting features, but that increasing the number of possible weights beyond two (zero and one) has very little benefit and sometimes degrades performance. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39126. 	[Title] Toward Optimal Feature Selection [Abstract] In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for defining the theoretically optimal, but computation-ally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it gives us little or no additional information beyond that subsumed by the remaining features. In particular, this will be the case for both irrelevant and redundant features. We then give an efficient algorithm for feature selection which computes an approximation to the optimal feature selection criterion. The conditions under which the approximate algorithm is successful are examined. Empirical results are given on a number of data sets, showing that the algorithm effectively han dles datasets with large numbers of features. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39127. 	[Title] Estimating Attributes: Analysis and Extensions of RELIEF [Abstract] In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very efficient in estimating attributes. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are verified on various artificial and one well known real-world problem. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128536. 	[Title] MLC Tutorial A Machine Learning library of C classes. [Abstract]  Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #40151. 	[Title] Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation [Abstract] Selecting a good model of a set of input points by cross validation is a computationally intensive process, especially if the number of possible models or the number of training points is high. Techniques such as gradient descent are helpful in searching through the space of models, but problems such as local minima, and more importantly, lack of a distance metric between various models reduce the applicability of these search methods. Hoeffding Races is a technique for finding a good model for the data by quickly discarding bad models, and concentrating the computational effort at differentiating between the better ones. This paper focuses on the special case of leave-one-out cross validation applied to memory-based learning algorithms, but we also argue that it is applicable to any class of model selection problems. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #39131. 	[Title] Oblivious Decision Trees and Abstract Cases [Abstract] In this paper, we address the problem of case-based learning in the presence of irrelevant features. We review previous work on attribute selection and present a new algorithm, Oblivion, that carries out greedy pruning of oblivious decision trees, which effectively store a set of abstract cases in memory. We hypothesize that this approach will efficiently identify relevant features even when they interact, as in parity concepts. We report experimental results on artificial domains that support this hypothesis, and experiments with natural domains that show improvement in some cases but not others. In closing, we discuss the implications of our experiments, consider additional work on irrelevant features, and outline some directions for future research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46431. 	[Title] Growing Simpler Decision Trees to Facilitate Knowledge Discovery [Abstract] When using machine learning techniques for knowledge discovery, output that is comprehensible to a human is as important as predictive accuracy. We introduce a new algorithm, SET-Gen, that improves the comprehensibility of decision trees grown by standard C4.5 without reducing accuracy. It does this by using genetic search to select the set of input features C4.5 is allowed to use to build its tree. We test SET-Gen on a wide variety of real-world datasets and show that SET-Gen trees are significantly smaller and reference significantly fewer features than trees grown by C4.5 without using SET-Gen. Statistical significance tests show that the accuracies of SET-Gen's trees are either not distinguishable from or are more accurate than those of the original C4.5 trees on all ten datasets tested. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #25184. 	[Title] Feature Selection via Mathematical Programming [Abstract] The problem of discriminating between two finite point sets in n-dimensional feature space by a separating plane that utilizes as few of the features as possible, is formulated as a mathematical program with a parametric objective function and linear constraints. The step function that appears in the objective function can be approximated by a sigmoid or by a concave exponential on the nonnegative real line, or it can be treated exactly by considering the equivalent linear program with equilibrium constraints (LPEC). Computational tests of these three approaches on publicly available real-world databases have been carried out and compared with an adaptation of the optimal brain damage (OBD) method for reducing neural network complexity. One feature selection algorithm via concave minimization (FSV) reduced cross-validation error on a cancer prognosis database by 35.4% while reducing problem features from 32 to 4. Feature selection is an important problem in machine learning [18, 15, 16, 17, 33]. In its basic form the problem consists of eliminating as many of the features in a given problem as possible, while still carrying out a preassigned task with acceptable accuracy. Having a minimal number of features often leads to better generalization and simpler models that can be more easily interpreted. In the present work, our task is to discriminate between two given sets in an n-dimensional feature space by using as few of the given features as possible. We shall formulate this problem as a mathematical program with a parametric objective function that will attempt to achieve this task by generating a separating plane in a feature space of as small a dimension as possible while minimizing the average distance of misclassified points to the plane. One of the computational experiments that we carried out on our feature selection procedure showed its effectiveness, not only in minimizing the number of features selected, but also in quickly recognizing and removing spurious random features that were introduced. Thus, on the Wisconsin Prognosis Breast Cancer WPBC database [36] with a feature space of 32 dimensions and 6 random features added, one of our algorithms FSV (11) immediately removed the 6 random features as well as 28 of the original features resulting in a separating plane in a 4-dimensional reduced feature space. By using tenfold cross-validation [35], separation error in the 4-dimensional space was reduced 35.4% from the corresponding error in the original problem space. (See Section 3 for details.) We note that mathematical programming approaches to the feature selection problem have been recently proposed in [4, 22]. Even though the approach of [4] is based on an LPEC formulation, both the LPEC and its method of solution are different from the ones used here. The polyhedral concave minimization approach of [22] is principally involved with theoretical considerations of one specific algorithm and no cross-validatory results are given. Other effective computational applications of mathematical programming to neural networks are given in [30, 26]. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128927. 	[Title] Machine learning in blood group determination of Danish Jersey cattle (causal probabilistic network). Dobljene mreze [Abstract] In the following paper we approach the problem with different machine learning algorithms and show that they can be compared with causal probabilistic networks in terms of performance and comprehensibility. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #52835. 	[Title] On Biases in Estimating Multi-Valued Attributes [Abstract] We analyse the biases of eleven measures for estimating the quality of the multi-valued attributes. The values of information gain, J-measure, gini-index, and relevance tend to linearly increase with the number of values of an attribute. The values of gain-ratio, distance measure, Relief , and the weight of evidence decrease for informative attributes and increase for irrelevant attributes. The bias of the statistic tests based on the chi-square distribution is similar but these functions are not able to discriminate among the attributes of different quality. We also introduce a new function based on the MDL principle whose value slightly decreases with the increasing number of attribute's values. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1128935. 	[Title] Prognosing the Survival Time of the Patients with the Anaplastic Thyroid Carcinoma with Machine Learning [Abstract] Anaplastic thyroid carcinoma is a rare but very aggressive tumor. Many factors that might influence the survival of patients have been suggested. The aim of our study was to determine which of the factors, known at the time of admission to the hospital, might predict survival of patients with anaplastic thyroid carcinoma. Our aim was also to assess the relative importance of the factors and to identify potentially useful decision and regression trees generated by machine learning algorithms. Our study included 126 patients (90 females and 36 males; mean age was 66.7 years) with anaplastic thyroid carcinoma treated at the Institute of Oncology Ljubljana from 1972 to 1992. Patients were classified into categories according to 11 attributes: sex, age, history, physical findings, extent of disease on admission, and tumor morphology. In this paper we compare the machine learning approach with the previous statistical evaluations on the problem (uni-variate and multivariate analysis) and show that it can provide more thorough analysis and improve understanding of the data. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #68463. 	[Title] Cost-sensitive feature reduction applied to a hybrid genetic algorithm [Abstract] This study is concerned with whether it is possible to detect what information contained in the training data and background knowledge is relevant for solving the learning problem, and whether irrelevant information can be eliminated in preprocessing before starting the learning process. A case study of data preprocessing for a hybrid genetic algorithm shows that the elimination of irrelevant features can substantially improve the efficiency of learning. In addition, cost-sensitive feature elimination can be effective for reducing costs of induced hypotheses. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #263279. 	[Title] Data Analyses Using Simulated Breeding and Inductive Learning Methods [Abstract] Marketing decision making tasks require the acquisition of efficient decision rules from noisy questionnaire data. Unlike popular learning-from-example methods, in such tasks, we must interpret the characteristics of the data without clear features of the data nor pre-determined evaluation criteria. The problem is how domain experts get simple, easy-to-understand, and accurate knowledge from noisy data. This paper describes a novel method to acquire efficient decision rules from questionnaire data using both simulated breeding and inductive learning techniques. The basic ideas of the method are that simulated breeding is used to get the effective features from the questionnaire data and that inductive learning is used to acquire simple decision rules from the data. The simulated breeding is one of the Genetic Algorithm based techniques to subjectively or interactively evaluate the qualities of offspring generated by genetic operations. The proposed method has been qualitatively and quantitatively validated by a case study on consumer product questionnaire data: the acquired rules are simpler than the results from the direct application of inductive learning; a domain expert admits that they are easy to understand; and they are at the same level on the accuracy compared with the other methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #116081. 	[Title] Induction of decision trees using RELIEFF [Abstract] In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies between them. Greedy search prevents current inductive machine learning algorithms to detect significant dependencies between the attributes. Recently, Kira and Rendell developed the RELIEF algorithm for estimating the quality of attributes that is able to detect dependencies between attributes. We show strong relation between RELIEF's estimates and impurity functions, that are usually used for heuristic guidance of inductive learning algorithms. We propose to use RELIEFF, an extended version of RELIEF, instead of myopic impurity functions. We have reimplemented Assistant, a system for top down induction of decision trees, using RELIEFF as an estimator of attributes at each selection step. The algorithm is tested on several artificial and several real world problems. Results show the advantage of the presented approach to inductive learning and open a wide rang of possibilities for using RELIEFF. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #20850. 	[Title] Compression-Based Feature Subset Selection  Keywords: Minimum Description Length Principle, Cross Validation, Noise [Abstract] Irrelevant and redundant features may reduce both predictive accuracy and comprehensibility of induced concepts. Most common Machine Learning approaches for selecting a good subset of relevant features rely on cross-validation. As an alternative, we present the application of a particular Minimum Description Length (MDL) measure to the task of feature subset selection. Using the MDL principle allows taking into account all of the available data at once. The new measure is information-theoretically plausible and yet still simple and therefore efficiently computable. We show empirically that this new method for judging the value of feature subsets is more efficient than and performs at least as well as methods based on cross-validation. Domains with both a large number of training examples and a large number of possible features yield the biggest gains in efficiency. Thus our new approach seems to scale up better to large learning problems than previous methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #116084. 	[Title] Linear Space Induction in First Order Logic with RELIEFF [Abstract] Current ILP algorithms typically use variants and extensions of the greedy search. This prevents them to detect significant relationships between the training objects. Instead of myopic impurity functions, we propose the use of the heuristic based on RELIEF for guidance of ILP algorithms. At each step, in our ILP-R system, this heuristic is used to determine a beam of candidate literals. The beam is then used in an exhaustive search for a potentially good conjunction of literals. From the efficiency point of view we introduce interesting declarative bias which enables us to keep the growth of the training set, when introducing new variables, within linear bounds (linear with respect to the clause length). This bias prohibits cross-referencing of variables in variable dependency tree. The resulting system has been tested on various artificial problems. The advantages and deficiencies of our approach are discussed. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63477. 	[Title] Efficient Feature Selection in Conceptual Clustering [Abstract] Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate the potential for similar benefits in an unsupervised learning task, conceptual clustering. The issues raised in feature selection by the absence of class labels are discussed and an implementation of a sequential feature selection algorithm based on an existing conceptual clustering system is described. Additionally, we present a second implementation which employs a technique for improving the efficiency of the search for an optimal description and compare the performance of both algorithms. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #167670. 	[Title] Error-Based and Entropy-Based Discretization of Continuous Features [Abstract] We present a comparison of error-based and entropy-based methods for discretization of continuous features. Our study includes both an extensive empirical comparison as well as an analysis of scenarios where error minimization may be an inappropriate discretization criterion. We present a discretization method based on the C4.5 decision tree algorithm and compare it to an existing entropy-based discretization algorithm, which employs the Minimum Description Length Principle, and a recently proposed error-based technique. We evaluate these discretization methods with respect to C4.5 and Naive-Bayesian classifiers on datasets from the UCI repository and analyze the computational complexity of each method. Our results indicate that the entropy-based MDL heuristic outperforms error minimization on average. We then analyze the shortcomings of error-based approaches in comparison to entropy-based methods. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8821. 	[Title] Machine Learning Methods for International Conflict Databases: A Case Study in Predicting Mediation Outcome [Abstract] This paper tries to identify rules and factors that are predictive for the outcome of international conflict management attempts. We use C4.5, an advanced Machine Learning algorithm, for generating decision trees and prediction rules from cases in the CONFMAN database. The results show that simple patterns and rules are often not only more understandable, but also more reliable than complex rules. Simple decision trees are able to improve the chances of correctly predicting the outcome of a conflict management attempt. This suggests that mediation is more repetitive than conflicts per se, where such results have not been achieved so far. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #8699. 	[Title] Theory Revision in Fault Hierarchies [Abstract] The fault hierarchy representation is widely used in expert systems for the diagnosis of complex mechanical devices. On the assumption that an appropriate bias for a knowledge representation language is also an appropriate bias for learning in this domain, we have developed a theory revision method that operates directly on a fault hierarchy. This task presents several challenges: A typical training instance is missing most feature values, and the pattern of missing features is significant, rather than merely an effect of noise. Moreover, the accuracy of a candidate theory is measured by considering both the sequence of tests required to arrive at a diagnosis and its agreement with the diagnostic endpoints provided by an expert. This paper first describes the algorithm for theory revision of fault hierarchies that was designed to address these challenges, then discusses its application in knowledge base maintenance and reports on experiments that use to revise a fielded diagnostic system. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #63486. 	[Title] Evaluation and Selection of Biases in Machine Learning [Abstract] In this introduction, we define the term bias as it is used in machine learning systems. We motivate the importance of automated methods for evaluating and selecting biases using a framework of bias selection as search in bias and meta-bias spaces. Recent research in the field of machine learning bias is summarized. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #116087. 	[Title] Discretization of continuous attributes using ReliefF [Abstract] Many existing learning algorithms expect the attributes to be discrete. Discretization of continuous attributes might be difficult task even for domain experts. We have tried the non-myopic heuristic measure ReliefF for discretization and compared it with well known dissimilarity measure and discretizations by experts. An extensive testing with several learning algorithms on six real world databases has shown that none of the discretizations has clear advantage over the others. Please summarise all the given information.

Paper #49660 has the title `Feature Subset Selection as Search with Probabilistic Estimates  
` and the abstract `Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of concepts induced by supervised learning algorithms. We formulate the search for a feature subset as an abstract search problem with probabilistic estimates. Searching a space using an evaluation function that is a random variable requires trading off accuracy of estimates for increased state exploration. We show how recent feature subset selection algorithms in the machine learning literature fit into this search problem as simple hill climbing approaches, and conduct a small experiment using a best-first search technique. 
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Question: Which category should Paper #49660 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #49660 has the title `Feature Subset Selection as Search with Probabilistic Estimates  
` and the abstract `Irrelevant features and weakly relevant features may reduce the comprehensibility and accuracy of concepts induced by supervised learning algorithms. We formulate the search for a feature subset as an abstract search problem with probabilistic estimates. Searching a space using an evaluation function that is a random variable requires trading off accuracy of estimates for increased state exploration. We show how recent feature subset selection algorithms in the machine learning literature fit into this search problem as simple hill climbing approaches, and conduct a small experiment using a best-first search technique. 
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Question: Which category should Paper #49660 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].

This paper should be classified into: Theory