Title

Sample Selection Through Class Probability and Possibility in Support Vector Machines

Abstract

Support vector machines (SVM) have shown attractive potential in classification. However, they have the limitation of size in training large data set and also sensitivity to outliers. In this paper, we used the class membership using the probability of each sample through K-nearest neighbors to reduce the training set. In SVM, if the training set contains outliers, support vectors might not be properly chosen and degrade the classification performance for unseen samples. In this case, the class membership assignment using probability is not appropriate to effectively eliminate outliers. To overcome this problem, we could check the possibility of the sample belonging to the class and eliminate the samples having weak possibility.

Department(s)

Engineering Management and Systems Engineering

Keywords and Phrases

Support Vector Machines (SVM); Vapnik-Chervonenkis (VC)

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2001 American Society of Mechanical Engineers (ASME), All rights reserved.

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