On the Handling of Continuous-Valued Attributes in Decision Tree Generation

We present a result applicable to classification learning algorithms that generate decision trees or rules using the information entropy minimization heuristic for discretizing continuous-valued attributes. The result serves to give a better understanding of the entropy measure, to point out that the behavior of the information entropy heuristic possesses desirable properties that justify its usage in a formal sense, and to improve the efficiency of evaluating continuous-valued attributes for cut value selection. Along with the formal proof, we present empirical results that demonstrate the theoretically expected reduction in evaluation effort for training data sets from real-world domains.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46964/1/10994_2004_Article_422458.pd

An experiment with association rules and classification: post-bagging and conviction

In this paper we study a new technique we call post-bagging, which consists in resampling parts of a classification model rather then the data. We do this with a particular kind of model: large sets of classification association rules, and in combination with ordinary best rule and weighted voting approaches. We empirically evaluate the effects of the technique in terms of classification accuracy. We also discuss the predictive power of different metrics used for association rule mining, such as confidence, lift, conviction and X². We conclude that, for the described experimental conditions, post-bagging improves classification results and that the best metric is conviction.Programa de Financiamento Plurianual de Unidades de I & D.Comunidade Europeia (CE). Fundo Europeu de Desenvolvimento Regional (FEDER).Fundação para a Ciência e a Tecnologia (FCT) - POSI/SRI/39630/2001/Class Project

Adaptive Discovery of Indexing Rules for Video

International audienceThis paper presents results, at an early stage of research work, of the use of fuzzy decision trees in a multimedia framework. We present the discovery of rules in three different indexing scenarios. These rules represent knowledge that can be interpreted as guidelines for the development of better indexing tools. We use a fuzzy decision tree algorithm to extract these rules (just) from color proportions of key-frames extracted from one video-news broadcast. Experimental results and comparisons with other data mining tools are presented