A hybrid decision tree/genetic algorithm method for data mining

Carvalho, Deborah R. and Freitas, Alex A. (2004) A hybrid decision tree/genetic algorithm method for data mining. Information Sciences, 163 (1-3). pp. 13-35. ISSN 0020-0255. (doi:https://doi.org/10.1016/j.ins.2003.03.013) (Full text available)

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This paper addresses the well-known classification task of data mining, where the objective is to predict the class which an example belongs to. Discovered knowledge is expressed in the form of high-level, easy-to-interpret classification rules. In order to discover classification rules, we propose a hybrid decision tree/genetic algorithm method. The central idea of this hybrid method involves the concept of small disjuncts in data mining, as follows. In essence, a set of classification rules can be regarded as a logical disjunction of rules, so that each rule can be regarded as a disjunct. A small disjunct is a rule covering a small number of examples. Due to their nature, small disjuncts are error prone. However, although each small disjunct covers just a few examples, the set of all small disjuncts can cover a large number of examples, so that it is important to develop new approaches to cope with the problem of small disjuncts. In our hybrid approach, we have developed two genetic algorithms (GA) specifically designed for discovering rules covering examples belonging to small disjuncts, whereas a conventional decision tree algorithm is used to produce rules covering examples belonging to large disjuncts. We present results evaluating the performance of the hybrid method in 22 real-world data sets.

Item Type: Article
Uncontrolled keywords: data mining, evolutionary algorithms, decision tree, classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:02 UTC
Last Modified: 19 May 2014 16:01 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/14144 (The current URI for this page, for reference purposes)
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