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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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 #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.

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 #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 #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 #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.

Paper #46476 has the title `A counter example to the stronger version of the binary tree hypothesis  
` and the 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. 
`.
Question: Which category should Paper #46476 be classified as ?
You can select one from ['Neural_Networks', 'Case_Based', 'Theory', 'Reinforcement_Learning', 'Probabilistic_Methods', 'Rule_Learning', 'Genetic_Algorithms'].


Paper #46476 has the title `A counter example to the stronger version of the binary tree hypothesis  
` and the 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. 
`.
Question: Which category should Paper #46476 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: Rule_Learning