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 #1114502. 	[Title] Evolution, Learning, and Instinct: 100 Years of the Baldwin Effect Using Learning to Facilitate the [Abstract] This paper describes a hybrid methodology that integrates genetic algorithms and decision tree learning in order to evolve useful subsets of discriminatory features for recognizing complex visual concepts. A genetic algorithm (GA) is used to search the space of all possible subsets of a large set of candidate discrimination features. Candidate feature subsets are evaluated by using C4.5, a decision-tree learning algorithm, to produce a decision tree based on the given features using a limited amount of training data. The classification performance of the resulting decision tree on unseen testing data is used as the fitness of the underlying feature subset. Experimental results are presented to show how increasing the amount of learning significantly improves feature set evolution for difficult visual recognition problems involving satellite and facial image data. In addition, we also report on the extent to which other more subtle aspects of the Baldwin effect are exhibited by the system. 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 #93320. 	[Title] Some Experiments with a Hybrid Model for Learning Sequential Decision Making [Abstract]  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 #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 #1135358. 	[Title] 0 Inductive learning of compact rule sets by using efficient hypotheses reduction [Abstract] A method is described which reduces the hypotheses space with an efficient and easily interpretable reduction criteria called a - reduction. A learning algorithm is described based on a - reduction and analyzed by using probability approximate correct learning results. The results are obtained by reducing a rule set to an equivalent set of kDNF formulas. The goal of the learning algorithm is to induce a compact rule set describing the basic dependencies within a set of data. The reduction is based on criterion which is very exible and gives a semantic interpretation of the rules which fulfill the criteria. Comparison with syntactical hypotheses reduction show that the a reduction improves search and has a smaller probability of missclassification. 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 #1107861. 	[Title] Boosting Trees for Cost-Sensitive Classifications [Abstract] This paper explores two boosting techniques for cost-sensitive tree classification in the situation where misclassification costs change very often. Ideally, one would like to have only one induction, and use the induced model for different misclassification costs. Thus, it demands robustness of the induced model against cost changes. Combining multiple trees gives robust predictions against this change. We demonstrate that ordinary boosting combined with the minimum expected cost criterion to select the prediction class is a good solution under this situation. We also introduce a variant of the ordinary boosting procedure which utilizes the cost information during training. We show that the proposed technique performs better than the ordinary boosting in terms of misclassification cost. However, this technique requires to induce a set of new trees every time the cost changes. Our empirical investigation also reveals some interesting behavior of boosting decision trees for cost-sensitive classification. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #180373. 	[Title] CANCER DIAGNOSIS AND PROGNOSIS VIA LINEAR-PROGRAMMING-BASED MACHINE LEARNING [Abstract]  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 #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 #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 #1102625. 	[Title] A systematic description of greedy optimisation algorithms for cost sensitive generalisation [Abstract] This paper defines a class of problems involving combinations of induction and (cost) optimisation. A framework is presented that systematically describes problems that involve construction of decision trees or rules, optimising accuracy as well as measurement- and misclassification costs. It does not present any new algorithms but shows how this framework can be used to configure greedy algorithms for constructing such trees or rules. The framework covers a number of existing algorithms. Moreover, the framework can also be used to define algorithm configurations with new functionalities, as expressed in their evaluation functions. 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 #1110947. 	[Title] Lazy Induction Triggered by CBR [Abstract] In recent years, case-based reasoning has been demonstrated to be highly useful for problem solving in complex domains. Also, mixed paradigm approaches emerged for combining CBR and induction techniques aiming at verifying the knowledge and/or building an efficient case memory. However, in complex domains induction over the whole problem space is often not possible or too time consuming. In this paper, an approach is presented which (owing to a close interaction with the CBR part) attempts to induce rules only for a particular context, i.e. for a problem just being solved by a CBR-oriented system. These rules may then be used for indexing purposes or similarity assessment in order to support the CBR process in the future. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1102751. 	[Title] The Challenge of Revising an Impure Theory [Abstract] A pure rule-based program will return a set of answers to each query; and will return the same answer set even if its rules are re-ordered. However, an impure program, which includes the Prolog cut "!" and not() operators, can return different answers if the rules are re-ordered. There are also many reasoning systems that return only the first answer found for each query; these first answers, too, depend on the rule order, even in pure rule-based systems. A theory revision algorithm, seeking a revised rule-base whose expected accuracy, over the distribution of queries, is optimal, should therefore consider modifying the order of the rules. This paper first shows that a polynomial number of training "labeled queries" (each a query coupled with its correct answer) provides the distribution information necessary to identify the optimal ordering. It then proves, however, that the task of determining which ordering is optimal, once given this information, is intractable even in trivial situations; e.g., even if each query is an atomic literal, we are seeking only a "perfect" theory, and the rule base is propositional. We also prove that this task is not even approximable: Unless P = N P , no polynomial time algorithm can produce an ordering of an n-rule theory whose accuracy is within n fl of optimal, for some fl &gt; 0. We also prove similar hardness, and non-approximatability, results for the related tasks of determining, in these impure contexts, (1) the optimal ordering of the antecedents; (2) the optimal set of rules to add or (3) to delete; and (4) the optimal priority values for a set of defaults. 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 #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 #10796. 	[Title] An Improved Algorithm for Incremental Induction of Decision Trees [Abstract] Technical Report 94-07 February 7, 1994 (updated April 25, 1994) This paper will appear in Proceedings of the Eleventh International Conference on Machine Learning. Abstract This paper presents an algorithm for incremental induction of decision trees that is able to handle both numeric and symbolic variables. In order to handle numeric variables, a new tree revision operator called `slewing' is introduced. Finally, a non-incremental method is given for finding a decision tree based on a direct metric of a candidate tree. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #10798. 	[Title] Learning physical descriptions from functional definitions, examples, Learning from examples: The effect of different conceptual [Abstract]  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 #30895. 	[Title] Induction of Oblique Decision Trees [Abstract] This paper introduces a randomized technique for partitioning examples using oblique hyperplanes. Standard decision tree techniques, such as ID3 and its descendants, partition a set of points with axis-parallel hyper-planes. Our method, by contrast, attempts to find hyperplanes at any orientation. The purpose of this more general technique is to find smaller but equally accurate decision trees than those created by other methods. We have tested our algorithm on both real and simulated data, and found that in some cases it produces surprisingly small trees without losing predictive accuracy. Small trees allow us, in turn, to obtain simple qualitative descriptions of each problem domain. 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 #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 #86840. 	[Title] Learning Active Classifiers [Abstract] Many classification algorithms are "passive", in that they assign a class-label to each instance based only on the description given, even if that description is incomplete. In contrast, an active classifier can | at some cost | obtain the values of missing attributes, before deciding upon a class label. The expected utility of using an active classifier depends on both the cost required to obtain the additional attribute values and the penalty incurred if it outputs the wrong classification. This paper considers the problem of learning near-optimal active classifiers, using a variant of the probably-approximately-correct (PAC) model. After defining the framework | which is perhaps the main contribution of this paper | we describe a situation where this task can be achieved efficiently, but then show that the task is often intractable. 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 #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 #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 #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 #6213. 	[Title] Machine Learning Learning to Predict by the Methods of Temporal Differences Keywords: Incremental learning, prediction, [Abstract] This article introduces a class of incremental learning procedures specialized for prediction|that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods; and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage. 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 #417017. 	[Title] Relating Relational Learning Algorithms [Abstract] Relational learning algorithms are of special interest to members of the machine learning community; they offer practical methods for extending the representations used in algorithms that solve supervised learning tasks. Five approaches are currently being explored to address issues involved with using relational representations. This paper surveys algorithms embodying these approaches, summarizes their empirical evaluations, highlights their commonalities, and suggests potential directions for future research. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #46536. 	[Title] Linear Machine Decision Trees [Abstract] COINS Technical Report 91-10 January 1991 Abstract This article presents an algorithm for inducing multiclass decision trees with multivariate tests at internal decision nodes. Each test is constructed by training a linear machine and eliminating variables in a controlled manner. Empirical results demonstrate that the algorithm builds small accurate trees across a variety of tasks. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #6217. 	[Title] Online Learning with Random Representations [Abstract] We consider the requirements of online learning|learning which must be done incrementally and in realtime, with the results of learning available soon after each new example is acquired. Despite the abundance of methods for learning from examples, there are few that can be used effectively for online learning, e.g., as components of reinforcement learning systems. Most of these few, including radial basis functions, CMACs, Ko-honen's self-organizing maps, and those developed in this paper, share the same structure. All expand the original input representation into a higher dimensional representation in an unsupervised way, and then map that representation to the final answer using a relatively simple supervised learner, such as a perceptron or LMS rule. Such structures learn very rapidly and reliably, but have been thought either to scale poorly or to require extensive domain knowledge. To the contrary, some researchers (Rosenblatt, 1962; Gallant & Smith, 1987; Kanerva, 1988; Prager & Fallside, 1988) have argued that the expanded representation can be chosen largely at random with good results. The main contribution of this paper is to develop and test this hypothesis. We show that simple random-representation methods can perform as well as nearest-neighbor methods (while being more suited to online learning), and significantly better than backpropagation. We find that the size of the random representation does increase with the dimensionality of the problem, but not unreasonably so, and that the required size can be reduced substantially using unsupervised-learning techniques. Our results suggest that randomness has a useful role to play in online supervised learning and constructive induction. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1113934. 	[Title] Integration of Case-Based Reasoning and Neural Networks Approaches for Classification [Abstract] Several different approaches have been used to describe concepts for supervised learning tasks. In this paper we describe two approaches which are: prototype-based incremental neural networks and case-based reasoning approaches. We show then how we can improve a prototype-based neural network model by storing some specific instances in a CBR memory system. This leads us to propose a co-processing hybrid model for classification. 1 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 #20942. 	[Title] Decision Tree Induction Based on Efficient Tree Restructuring [Abstract] The ability to restructure a decision tree efficiently enables a variety of approaches to decision tree induction that would otherwise be prohibitively expensive. Two such approaches are described here, one being incremental tree induction (ITI), and the other being non-incremental tree induction using a measure of tree quality instead of test quality (DMTI). These approaches and several variants offer new computational and classifier characteristics that lend themselves to particular applications. 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 #5966. 	[Title] Cost-Sensitive Classification: Empirical Evaluation of a Hybrid Genetic Decision Tree Induction Algorithm [Abstract] This paper introduces ICET, a new algorithm for costsensitive classification. ICET uses a genetic algorithm to evolve a population of biases for a decision tree induction algorithm. The fitness function of the genetic algorithm is the average cost of classification when using the decision tree, including both the costs of tests (features, measurements) and the costs of classification errors. ICET is compared here with three other algorithms for costsensitive classification EG2, CS-ID3, and IDX and also with C4.5, which classifies without regard to cost. The five algorithms are evaluated empirically on five real-world medical datasets. Three sets of experiments are performed. The first set examines the baseline performance of the five algorithms on the five datasets and establishes that ICET performs significantly better than its competitors. The second set tests the robustness of ICET under a variety of conditions and shows that ICET maintains its advantage. The third set looks at ICETs search in bias space and discovers a way to improve the search. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #1106388. 	[Title] Predicting Ordinal Classes in ILP [Abstract] This paper is devoted to the problem of learning to predict ordinal (i.e., ordered discrete) classes in an ILP setting. We start with a relational regression algorithm named SRT (Structural Regression Trees) and study various ways of transforming it into a first-order learner for ordinal classification tasks. Combinations of these algorithm variants with several data preprocessing methods are compared on two ILP benchmark data sets to verify the relative strengths and weaknesses of the strategies and to study the trade-off between optimal categorical classification accuracy (hit rate) and minimum distance-based error. Preliminary results indicate that this is a promising avenue towards algorithms that combine aspects of classification and regression in relational learning. 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 #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 #601567. 	[Title] Geometric Comparison of Classifications and Rule Sets* [Abstract] We present a technique for evaluating classifications by geometric comparison of rule sets. Rules are represented as objects in an n-dimensional hyperspace. The similarity of classes is computed from the overlap of the geometric class descriptions. The system produces a correlation matrix that indicates the degree of similarity between each pair of classes. The technique can be applied to classifications generated by different algorithms, with different numbers of classes and different attribute sets. Experimental results from a case study in a medical domain are included. 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 #8696. 	[Title] The Complexity of Theory Revision [Abstract] A knowledge-based system uses its database (a.k.a. its "theory") to produce answers to the queries it receives. Unfortunately, these answers may be incorrect if the underlying theory is faulty. Standard "theory revision" systems use a given set of "labeled queries" (each a query paired with its correct answer) to transform the given theory, by adding and/or deleting either rules and/or antecedents, into a related theory that is as accurate as possible. After formally defining the theory revision task and bounding its sample complexity, this paper addresses the task's computational complexity. It first proves that, unless P = N P , no polynomial time algorithm can identify the optimal theory, even given the exact distribution of queries, except in the most trivial of situations. It also shows that, except in such trivial situations, no polynomial-time algorithm can produce a theory whose inaccuracy is even close (i.e., within a particular polynomial factor) to optimal. These results justify the standard practice of hill-climbing to a locally-optimal theory, based on a given set of labeled sam ples. 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 #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 #8687. 	[Title] The Difficulties of Learning Logic Programs with Cut [Abstract] As real logic programmers normally use cut (!), an effective learning procedure for logic programs should be able to deal with it. Because the cut predicate has only a procedural meaning, clauses containing cut cannot be learned using an extensional evaluation method, as is done in most learning systems. On the other hand, searching a space of possible programs (instead of a space of independent clauses) is unfeasible. An alternative solution is to generate first a candidate base program which covers the positive examples, and then make it consistent by inserting cut where appropriate. The problem of learning programs with cut has not been investigated before and this seems to be a natural and reasonable approach. We generalize this scheme and investigate the difficulties that arise. Some of the major shortcomings are actually caused, in general, by the need for intensional evaluation. As a conclusion, the analysis of this paper suggests, on precise and technical grounds, that learning cut is difficult, and current induction techniques should probably be restricted to purely declarative logic languages. 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 #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 #1110390. 	[Title] How to Get a Free Lunch: A Simple Cost Model for Machine Learning Applications [Abstract] This paper proposes a simple cost model for machine learning applications based on the notion of net present value. The model extends and unifies the models used in (Pazzani et al., 1994) and (Masand & Piatetsky-Shapiro, 1996). It attempts to answer the question "Should a given machine learning system now in the prototype stage be fielded?" The model's inputs are the system's confusion matrix, the cash flow matrix for the application, the cost per decision, the one-time cost of deploying the system, and the rate of return on investment. Like Provost and Fawcett's (1997) ROC convex hull method, the present model can be used for decision-making even when its input variables are not known exactly. Despite its simplicity, it has a number of non-trivial consequences. For example, under it the "no free lunch" theorems of learning theory no longer apply. Please summarise all the given information.

Above are papers related to the following paper: [Paper_ID] Paper #887. 	[Title] Some studies in machine learning using the game of checkers. IBM Journal, 3(3):211-229, 1959. Some [Abstract]  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 #31097. 	[Title] What Daimler-Benz has learned as an industrial partner from the Machine Learning Project StatLog [Abstract] Author of this paper was co-ordinator of the Machine Learning project StatLog during 1990-1993. This project was supported financially by the European Community. The main aim of StatLog was to evaluate different learning algorithms using real industrial and commercial applications. As an industrial partner and contributor, Daimler-Benz has introduced different applications to Stat-Log among them fault diagnosis, letter and digit recognition, credit-scoring and prediction of the number of registered trucks. We have learned a lot of lessons from this project which have effected our application oriented research in the field of Machine Learning (ML) in Daimler-Benz. We have distinguished that, especially, more research is necessary to prepare the ML-algorithms to handle the real industrial and commercial applications. In this paper we describe, shortly, the Daimler-Benz applications in StatLog, we discuss shortcomings of the applied ML-algorithms and finally we outline the fields where we think further research is necessary. 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 #1153148. 	[Title] Learning Classification Trees [Abstract] Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. This paper outlines how a tree learning algorithm can be derived using Bayesian statistics. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule is similar to Quinlan's information gain, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 (Quinlan et al., 1987) and Breiman et al.'s CART (Breiman et al., 1984) show the full Bayesian algorithm can produce Publication: This paper is a final draft submitted for publication to the Statistics and Computing journal; a version with some minor changes appeared in Volume 2, 1992, pages 63-73. more accurate predictions than versions of these other approaches, though pay a computational price. 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 #8703. 	[Title] Theory Refinement Combining Analytical and Empirical Methods [Abstract] This article describes a comprehensive approach to automatic theory revision. Given an imperfect theory, the approach combines explanation attempts for incorrectly classified examples in order to identify the failing portions of the theory. For each theory fault, correlated subsets of the examples are used to inductively generate a correction. Because the corrections are focused, they tend to preserve the structure of the original theory. Because the system starts with an approximate domain theory, in general fewer training examples are required to attain a given level of performance (classification accuracy) compared to a purely empirical system. The approach applies to classification systems employing a propositional Horn-clause theory. The system has been tested in a variety of application domains, and results are presented for problems in the domains of molecular biology and plant disease diagnosis. Please summarise all the given information.

Paper #8699 has the title `Theory Revision in Fault Hierarchies  
` and the 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. 
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Question: Which category should Paper #8699 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #8699 has the title `Theory Revision in Fault Hierarchies  
` and the 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. 
`.
Question: Which category should Paper #8699 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