Data selection based on decision tree for SVM classification on large data sets
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- 作者：Jair Cervantesa ; jcervantesc@uaemex.mx" class="auth_mail" title="E-mail the corresponding author ; Farid Garcí ; a Lamonta ; Asdrú ; bal Ló ; pez-Chaub ; Lisbeth Rodrí ; guez Mazahuac ; J. Sergio Ruí ; za
- 关键词：SVM ; Classification ; Large data sets
- 刊名：Applied Soft Computing
- 出版年：2015
- 出版时间：December 2015
- 年：2015
- 卷：37
- 期：Complete
- 页码：787-798
- 全文大小：987 K
文摘
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This paper describes the development of an algorithm for training large data sets.
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The algorithm uses a first stage of SVM with a small data set.
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The algorithm uses decision trees to find best data points in the entire data set.
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DT is trained using SV and non-SV found in the first SVM stage.
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In the second SVM stage the training data represent all data points found by the DT.