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- W2388544830 abstract "This paper presented a new GA based Subspace classification algorithm for SVM(GS-SVM).A modified sample selection method is adopted to select a subset of training data based on both the confidence and the convex hull.Then the representative samples are selected to train the SVM models by considering the distances between classes and the sample distribution.The algorithm adopts the matrix-form mixed encoding.Genetic algorithm is used to optimize the feature subspace of representative samples and the classification parameters of SVM simultaneously.The SVM classification model is produced based on the representative samples with the optimized feature subspace.Experimental results on eleven UCI datasets illustrate that the proposed algorithm is able to select both smaller sample subset and feature size,and achieve higher classification accuracy than the traditional classification algorithms." @default.
- W2388544830 created "2016-06-24" @default.
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- W2388544830 date "2013-01-01" @default.
- W2388544830 modified "2023-09-25" @default.
- W2388544830 title "GA-based Subspace Classification Algorithm for Support Vector Machines" @default.
- W2388544830 hasPublicationYear "2013" @default.
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