Learning non-overlapping rules - A method based on Functional Dependency Network and MDL Genetic Programming
Refereed conference paper presented and published in conference proceedings


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摘要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.
著者Shum WH, Leung KS, Wong ML
會議名稱IEEE Congress on Evolutionary Computation
會議開始日16.07.2006
會議完結日21.07.2006
會議地點Vancouver
會議國家/地區加拿大
詳細描述organized by IEEE,
出版年份2006
月份1
日期1
出版社IEEE
頁次702 - 709
國際標準書號978-0-7803-9487-2
語言英式英語
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods

上次更新時間 2021-03-04 於 00:28