Evaluating six candidate solutions for the small-disjunct problem and choosing the best solution via meta-learning

Carvalho, Deborah R. and Freitas, Alex A. (2005) Evaluating six candidate solutions for the small-disjunct problem and choosing the best solution via meta-learning. Artificial Intelligence Review, 24 (1). pp. 61-98. ISSN 0269-2821. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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A set of classification rules can be considered as a disjunction of rules, where each rule is a disjunct. A small disjunct is a rule covering a small number of examples. Small disjuncts are a serious problem for effective classification, because the small number of examples satisfying these rules makes their prediction unreliable and error-prone. This paper offers two main contributions to the research on small disjuncts. First, it investigates six candidate solutions (algorithms) for the problem of small disjuncts. Second, it reports the results of a meta-learning experiment, which produced meta-rules predicting which algorithm will tend to perform best for a given data set. The algorithms investigated in this paper belong to different machine learning paradigms and their hybrid combinations, as follows: two versions of a decision-tree (DT) induction algorithm; two versions of a hybrid DT/genetic algorithm (GA) method; one GA; one hybrid DT/instance-based learning (IBL) algorithm. Experiments with 22 data sets evaluated both the predictive accuracy and the simplicity of the discovered rule sets, with the following conclusions. If one wants to maximize predictive accuracy only, then the hybrid DT/IBL seems to be the best choice. On the other hand, if one wants to maximize both predictive accuracy and rule set simplicity - which is important in the context of data mining - then a hybrid DT/GA seems to be the best choice.

Item Type: Article
Uncontrolled keywords: data mining, classification, evolutionary algorithms, decision tree, meta-learning
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:02
Last Modified: 20 May 2014 08:19
Resource URI: https://kar.kent.ac.uk/id/eprint/14257 (The current URI for this page, for reference purposes)
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