Explanation of SVM's behaviour in text classification

author: Fabrice Colas, Leiden Institute of Advanced Computer Science, Leiden University
published: Oct. 24, 2007,   recorded: September 2007,   views: 1528

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We are concerned with the problem of learning classification rules in text categorization where many authors presented Support Vector Machines (SVM) as leading classification method. Number of studies, however, repeatedly pointed out that in some situations SVM is outperformed by simpler methods such as naive Bayes or nearest-neighbor rule. In this paper, we aim at developing better understanding of SVM behaviour in typical text categorization problems represented by sparse bag of words feature spaces. We study in details the performance and the number of support vectors when varying the training set size, the number of features and, unlike existing studies, also SVM free parameter C, which is the Lagrange multipliers upper bound in SVM dual. We show that SVM solutions with small C are high performers. However, most training documents are then bounded support vectors sharing a same weight C . Thus, SVM reduce to a nearest mean classifier; this raises an interesting question on SVM merits in sparse bag of words feature spaces. Additionally, SVM suffer from performance deterioration for particular training set size/number of features combinations.

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Reviews and comments:

Comment1 Fabrice Colas, December 19, 2010 at 9:06 p.m.:

If you like to go further with the research questions discussed in this talk, i.e. the consistent occurrence of a performance drop for SVM for particular combinations of feature space sizes and number of documents, I went on slightly further in the months that followed this talk. As reference, I would suggest Chapter 8 of my PhD thesis. It is available on-line as PDF.

For complete references, see web-page of the text classification study:

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