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A Classifier Based on a Decision Tree with Verifying Cuts

Bazan J. G., Buregwa-Czuma S., Rzasa W., Bazan-Socha S., Dydo L., Skowron A.

Fundamenta Informaticae

2016

Vol. 143, nr 1/2

1--18

This article introduces a new method of a decision tree construction. Such construction is performed using additional cuts applied for a verification of the cuts' quality in tree nodes during the classification of objects. The presented approach allows us to exploit the additional knowledge represented in the attributes which could be eliminated using greedy methods. The paper includes the results of experiments performed on data sets from a biomedical database and machine learning repositories. In order to evaluate the presented method, we compared its performance with the classification results of a local discretization decision tree, well known from literature. Our new method out performs the existing method, which is also confirmed by statistical tests.

A Local Version of the MLEM2 Algorithm for Rule Induction

Grzymala-Busse J.W., Rzasa W.

Fundamenta Informaticae

2010

Vol. 100, nr 1/4

99-116

In this paper, we present the newest version of the MLEM2 algorithm for rule induction, a basic component of the LERS data mining system. This version of the MLEM2 algorithm is based on local lower and upper approximations, and in its current formis presented in this paper for the first time. Additionally, we present results of experiments comparing the local version of the MLEM2 algorithm for rule induction with an older version of MLEM2, which was based on global lower and upper approximations. Our experiments show that the local version of MLEM2 is significantly better than the global version of MLEM2 (2% significance level, two-tailed Wilcoxon test).