Journal
PATTERN RECOGNITION
Volume 48, Issue 6, Pages 2072-2083Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2014.12.015
Keywords
Support vector machine; Heterogeneous data; Nominal attribute; Numerical attribute; Classification learning
Funding
- National Program on Key Basic Research Project [2013CB329304]
- National Natural Science Foundation of China [61222210, 61432011]
- New Century Excellent Talents in University [NCET-12-0399]
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A support vector machine (SVM) is a popular algorithm for classification learning. The classical SVM effectively manages classification tasks defined by means of numerical attributes. However, both numerical and nominal attributes are used in practical tasks and the classical SVM does not fully consider the difference between them. Nominal attributes are usually regarded as numerical after coding. This may deteriorate the performance of learning algorithms. In this study, we propose a novel SVM algorithm for learning with heterogeneous data, known as a heterogeneous SVM (HSVM). The proposed algorithm learns an mapping to embed nominal attributes into a real space by minimizing an estimated generalization error, instead of by direct coding. Extensive experiments are conducted, and some interesting results are obtained. The experiments show that HSVM improves classification performance for both nominal and heterogeneous data. (C) 2014 Elsevier Ltd. All rights reserved.
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