Title

Learning non-overlapping rules a method based on functional dependency network and mdl genetic programming

Document Type

Conference paper

Source Publication

Proceedings of the 2006 IEEE Congress on Evolutionary Computation, CEC 2006

Publication Date

1-1-2006

First Page

702

Last Page

709

Publisher

Institute of Electrical and Electronics Engineers

Abstract

Classification rule is a useful model in data mining. Given variable values, rules classify data items into different classes. Different rule learning algorithms are proposed, like Genetic Algorithm (GA) and Genetic Programming (GP). Rules can also be extracted from Bayesian Network (BN) and decision trees. However, all of them have disadvantages and may fail to get the best results. Both of GA and GP cannot handle cooperation among rules and thus, the learnt rules are likely to have many overlappings, i.e. more than one rules classify the same data items and different rules have different predictions. The conflicts among the rules reduce their understandability and increase their usage difficulty for expert systems. In contrast, rules extracted from BN and decision trees have no overlapping in nature. But BN can handle discrete values only and cannot represent higher-order relationships among variables. Moreover, the search space for decision tree learning is huge and thus, it is difficult to reach the global optimum. In this paper, we propose to use Functional Dependency Network (FDN) and MDL Genetic Programming (MDLGP) to learn a set of non-overlapping classification rules [17]. The FDN is an extension of BN; it can handle all kind of values; it can represent higher-order relationships among variables; and its learning search space is smaller than decision trees'. The experimental results demonstrate that the proposed method can successfully discover the target rules, which have no overlapping and have the highest classification accuracies.

DOI

10.1109/CEC.2006.1688380

Publisher Statement

Copyright © 2006 IEEE. Access to external full text or publisher's version may require subscription.

Additional Information

Paper presented at the 2006 IEEE Congress on Evolutionary Computation (CEC 2006), 16-21 July 2006, Vancouver, Canada.

ISBN of the source publication: 9780780394872

Full-text Version

Publisher’s Version

Language

English

Recommended Citation

Shum, W.-H., Leung, K.-S., & Wong, M.-L. (2006). Learning non-overlapping rules a method based on functional dependency network and mdl genetic programming. In Proceedings of the 2006 IEEE Congress on Evolutionary Computation, CEC 2006 (pp. 702-709). Piscataway: Institute of Electrical and Electronics Engineers. doi: 10.1109/CEC.2006.1688380

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