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

Wing Ho SHUM, Kwong Sak LEUNG, Man Leung WONG

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review


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.
Original languageEnglish
Title of host publicationProceedings of the 2006 IEEE Congress on Evolutionary Computation, CEC 2006
PublisherInstitute of Electrical and Electronics Engineers
Number of pages8
Publication statusPublished - 1 Jan 2006

Bibliographical note

Paper presented at the 2006 IEEE Congress on Evolutionary Computation (CEC 2006), 16-21 July 2006, Vancouver, Canada.
ISBN of the source publication: 9780780394872


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