NDLI: A Novel Genetic Algorithm for Subspace Based Subclasssifier Selection

Content Provider	IEEE Xplore Digital Library
Author	Fei Wang Ming Yang
Copyright Year	2009
Abstract	Ensemble learning constitutes one of the most popular directions in machine learning and data mining currently. And in ensemble learning, feature subspace selection and corresponding classifier ensemble for classification becomes the principal topic, in which base classifiers(also called subclassifiers) are generated by different subspaces. However, very little work has been done for effectively selecting the subclassifiers induced by different subspaces. In this paper, we introduce a novel Genetic Algorithm for subspace ensemble based subclassifier selection, that is, the newly developed algorithm attempts to select significant and relevant subclassifiers using genetic algorithm for improving the classification performance of ensemble. The experimental results show that the algorithm of this paper has better or comparable performance than those obtained by the well-known ensemble methods such as Bagging, AdaBoost and Random Subspace. Of course, how to determine the number of subclassifiers and the parameters used in genetic algorithm is our ongoing work.
Starting Page	513
Ending Page	517
File Size	906809
Page Count	5
File Format	PDF
ISBN	9780769537368
DOI	10.1109/ICNC.2009.376
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-08-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ant colony optimization Fitness Function Genetic Algorithm Partitioning algorithms Data mining Genetic algorithms Learning systems Computer science Accuracy Machine learning Subclassifier Selection Ensemble learning Bagging Classification tree analysis
Content Type	Text
Resource Type	Article

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