Learning compositional decision rules using the KEX algorithm

Article type: Research Article

Authors: Berka, Petr

Affiliations: Department of Information and Knowledge Engineering, University of Economics, W. Churchill Sq.~4, 130 67 Prague, Czech Republic. Tel.: +420 224 095 493; Fax: +420 224 095 400; E-mail: [email protected]

Abstract: "If-then" rules belong to the most popular formalism used to represent knowledge either obtained from human experts (as in the case of expert systems) or learned from data (as in the case of machine learning and data mining). The most commonly used approach to learning decision rules is the set-covering approach, also called "separate and conquer". The other way to create decision rules is the compositional approach. The work reported in this paper fits into the latter approach. We will describe the KEX algorithm, its implementation within the LISp-Miner system, and results of empirical comparison of KEX with some other rule-learning algorithms implemented in the Weka system.

Keywords: Classification, decision rules, KEX, LISp-Miner

DOI: 10.3233/IDA-2012-0543

Journal: Intelligent Data Analysis, vol. 16, no. 4, pp. 665-681, 2012