The classification rules induced by machine learning systems are judged by two criteria: their classification accuracy on an independent test set (henceforth "accuracy"), and their complexity. The relationship between these two criteria is, of course, of keen interest

Classification rule mining aims to discover a small set of rules in the database that forms an accurate classifier. Association rule mining finds all the rules existing in the database that satisfy some minimum support and minimum confidence constraints. For association rule mining, the target

The main contributions of this thesis are a Bayesian theory of learning classification rules, the unification and comparison of this theory with some previous theories of learning, and two extensive applications of the theory to the problems of learning class probability trees and bounding error

A granular computing model is used for learning classification rules by considering the two basic issues: concept formation and concept relationships identification. A classification rule induction method is proposed. Instead of focusing on the selection of a suitable partition, i.e., a family

Summary. In this chapter, we discuss the application of evolutionary multiob-jective optimization (EMO) to association rule mining. Especially, we focus our attention on classification rule mining in a continuous feature space where the an-tecedent and consequent parts of each rule are an interval

This paper is a discussion of machine learning theory on empirically learning classification rules. The paper proposes six myths in the machine lea.n_ng community that address issues of bias, learning as search, computational learning theory, Occam's razor, _universal " learning

maximum likelihood estimator. Next, we consider more complicated classification rules based on averaging over a class of base classifiers. We allow that certain examples are not classified due to lack of evidence, and provide a uniform bound for the margin. Moreover, we illustrate that when using averaged

Abstract. This paper highlights the interest of implicative statistics for classification trees. We start by showing how Gras ’ implication index may be defined for the rules derived from an induced decision tree. Then, we show that residuals used in the modeling of contingency tables provide

Abstract. Inducing classification rules on domains from which information is gathered at regular periods lead the number of such classification rules to be generally so huge that selection of interesting ones among all discovered rules becomes an important task. At each period, using the newly

“Selection of variables for cluster analysis and classification rules”