Abstract
Text Categorization (TC) remains as a potential application area for linear support vector machines (SVMs). Among the numerous linear SVM formulations, we bring forward linear PSVM together with recently proposed distributional clustering (DC) of words to realize its potential in TC realm. DC has been presented as an efficient alternative to conventionally used feature selection in TC. It has been shown that, DC together with linear SVM drastically brings down the dimensionality of text documents without any compromise in classification performance. In this paper we use linear PSVM and its extension Fuzzy PSVM (FPSVM) together with DC for TC. We present experimental results comparing PSVM/FPSVM with linear SVMlight and SVMlin on popular WebKB text corpus. Through numerous experiments on subsets of WebKB, we reveal the merits of PSVM and FPSVM over other linear SVMs.
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Kumar, M.A., Gopal, M. (2009). Text Categorization Using Fuzzy Proximal SVM and Distributional Clustering of Words. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_8
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DOI: https://doi.org/10.1007/978-3-642-01307-2_8
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