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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer-Verlag

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DOI

10.1016/0009-2797(84)90079-6

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1433-3058

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Application of featureweighted Support Vector reg

Authors: James N K Liu Yanxing Hu
Publish Date: 2012/06/30
Volume: 22, Issue: 1, Pages: 143-152
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Abstract

A featureweighted Support Vector Machine regression algorithm is introduced in this paper We note that the classical SVM is based on the assumption that all the features of the sample points supply the same contribution to the target output value However this assumption is not always true in real problems In the proposed new algorithm we give different weight values to different features of the samples in order to improve the performance of SVM In our algorithm firstly a measure named grey correlation degree is applied to evaluate the correlation between each feature and the target problem and then the values of the grey correlation degree are used as weight values assigned to the features The proposed method is tested on sample stock data sets selected from China Shenzhen Ashare market The result shows that the new version of SVM can improve the accuracy of the prediction

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