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APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
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Wavelet transformation and cluster ensemble for gene expression analysis
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ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
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In this paper we present a new approach to construct a good ensemble of classifiers using rough sets theory and database operations. Ensembles of classifiers is formulated precisely within the framework of rough sets theory and constructed very efficiently by using set-oriented database operations. Our method first computes a set of reductswhich include all the indispensable attributes required for the decision categories. For each reduct, a reduct table is generated by removing those attributes which are not in the reduct. Next, a novel rule induction algorithm is used to compute the maximal generalized rules for each reducttable and a set of reduct classifiers is formed based on thecorresponding reducts. The distinctive features of our method as compared to other methods of constructing ensembles of classifiers are:(1) present a theoretical model to explain the mechanism of constructing ensemble of classifiers, (2) each reduct is a minimum subset of attributes, has the same classification ability as the entire attributes,(3)ea h reduct classifier constructed from the corresponding reduct has a minimal set of classification rules, and is as accurate andcomplete as possible and at the same time as diverse as possible from the other classifiers, (4)the test indicates that the number of classifiers used to improve the accuracy is muchless than other methods