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Efficient Learning of Selective Bayesian Network Classifiers
, 1995
"... In this paper, we present a computationally efficient method for inducing selective Bayesian network classifiers. Our approach is to use informationtheoretic metrics to efficiently select a subset of attributes from which to learn the classifier. We explore three conditional, informationtheoretic ..."
Abstract

Cited by 49 (4 self)
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In this paper, we present a computationally efficient method for inducing selective Bayesian network classifiers. Our approach is to use informationtheoretic metrics to efficiently select a subset of attributes from which to learn the classifier. We explore three conditional, informationtheoretic metrics that are extensions of metrics used extensively in decision tree learning, namely Quinlan's gain and gain ratio metrics and Mantaras's distance metric. We experimentally show that the algorithms based on gain ratio and distance metric learn selective Bayesian networks that have predictive accuracies as good as or better than those learned by existing selective Bayesian network induction approaches (K2AS), but at a significantly lower computational cost. We prove that the subsetselection phase of these informationbased algorithms has polynomial complexity as compared to the worstcase exponential time complexity of the corresponding phase in K2AS. We also compare the performance o...
A Comparison of Induction Algorithms for Selective and nonSelective Bayesian Classifiers
 Proceedings of the Twelfth International Conference on Machine Learning
, 1995
"... In this paper we present a novel induction algorithm for Bayesian networks. This selective Bayesian network classifier selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby learning Bayesian networks with a bias for small, highpredictiveaccu ..."
Abstract

Cited by 24 (5 self)
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In this paper we present a novel induction algorithm for Bayesian networks. This selective Bayesian network classifier selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby learning Bayesian networks with a bias for small, highpredictiveaccuracy networks. We compare the performance of this classifier with selective and nonselective naive Bayesian classifiers. We show that the selective Bayesian network classifier performs significantly better than both versions of the naive Bayesian classifier on almost all databases analyzed, and hence is an enhancement of the naive Bayesian classifier. Relative to the nonselective Bayesian network classifier, our selective Bayesian network classifier generates networks that are computationally simpler to evaluate and that display predictive accuracy comparable to that of Bayesian networks which model all features. 1 INTRODUCTION Bayesian induction methods have proven to be an important cla...
Induction of Selective Bayesian Network Classifiers
, 1996
"... We present an algorithm for inducing Bayesian networks using feature selection. The algorithm selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby incorporating a bias for small networks that retain high predictive accuracy. We compare the be ..."
Abstract

Cited by 2 (0 self)
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We present an algorithm for inducing Bayesian networks using feature selection. The algorithm selects a subset of attributes that maximizes predictive accuracy prior to the network learning phase, thereby incorporating a bias for small networks that retain high predictive accuracy. We compare the behavior of this selective Bayesian network classifier with that of (a) Bayesian network classifiers that incorporate all attributes, (b) selective and nonselective naive Bayesian classifiers, and (c) the decisiontree algorithm C4.5. With respect to (a), we show that our approach generates networks that are computationally simpler to evaluate but display comparable predictive accuracy. With respect to (b), we show that the selective Bayesian network classifier performs significantly better than both versions of the naive Bayesian classifier on almost all databases studied, and hence is an enhancement of the naive method. With respect to (c), we show that the selective Bayesian network class...