A Classifier Based on a Decision Tree with Verifying Cuts

Issue title: Concurrency, Specification and Programming (CS&P)

Guest editors: Ludwik Czaja, Wojciech Penczek and Krzysztof Stencel

Article type: Research Article

Authors: Bazan, Jan G.^{a; *} | Bazan-Socha, Stanislawa^b | Buregwa-Czuma, Sylwia^c | Dydo, Lukasz^d | Rzasa, Wojciech^e | Skowron, Andrzej^f

Affiliations: [a] Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland. [email protected] | [b] II Department of Internal Medicine, Jagiellonian University Medical College, Skawinska 8, 31-066 Krakow, Poland. [email protected] | [c] Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland. [email protected] | [d] Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland. [email protected] | [e] Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland. [email protected] | [f] Institute of Mathematics, University of Warsaw, Banacha 2, 02-097 Warsaw, Poland, and Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01�447 Warsaw, Poland. [email protected]

Correspondence: [*] Address for correspondence: Interdisciplinary Centre for Computational Modelling, University of Rzeszów, Pigonia 1, 35-310 Rzeszów, Poland

Abstract: 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 outperforms the existing method, which is also confirmed by statistical tests.

Keywords: rough sets, discretization, concept approximation, classifiers

DOI: 10.3233/FI-2016-1300

Journal: Fundamenta Informaticae, vol. 143, no. 1-2, pp. 1-18, 2016