TY - JOUR

T1 - One-Class SVMs for Document Classification

AU - Manevitz, Larry M.

AU - Yousef, Malik

N1 - Publisher Copyright:
© 2001 Larry M. Manevitz and Malik Yousef.

PY - 2002

Y1 - 2002

N2 - We implemented versions of the SVM appropriate for one-class classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schölkopf et al. and a somewhat different version of one-class SVM based on identifying “outlier” data as representative of the second-class. We report on experiments with different kernels for both of these implementations and with different representations of the data, including binary vectors, tf-idf representation and a modification called “Hadamard” representation. Then we compared it with one-class versions of the algorithms prototype (Rocchio), nearest neighbor, naive Bayes, and finally a natural one-class neural network classification method based on “bottleneck” compression generated filters. The SVM approach as represented by Schölkopf was superior to all the methods except the neural network one, where it was, although occasionally worse, essentially comparable. However, the SVM methods turned out to be quite sensitive to the choice of representation and kernel in ways which are not well understood; therefore, for the time being leaving the neural network approach as the most robust.

AB - We implemented versions of the SVM appropriate for one-class classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schölkopf et al. and a somewhat different version of one-class SVM based on identifying “outlier” data as representative of the second-class. We report on experiments with different kernels for both of these implementations and with different representations of the data, including binary vectors, tf-idf representation and a modification called “Hadamard” representation. Then we compared it with one-class versions of the algorithms prototype (Rocchio), nearest neighbor, naive Bayes, and finally a natural one-class neural network classification method based on “bottleneck” compression generated filters. The SVM approach as represented by Schölkopf was superior to all the methods except the neural network one, where it was, although occasionally worse, essentially comparable. However, the SVM methods turned out to be quite sensitive to the choice of representation and kernel in ways which are not well understood; therefore, for the time being leaving the neural network approach as the most robust.

KW - Compression Neural Network

KW - Neural Network

KW - Positive Information

KW - Support Vector Machine

KW - SVM

KW - Text Retrieval

UR - http://www.scopus.com/inward/record.url?scp=85096855936&partnerID=8YFLogxK

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AN - SCOPUS:85096855936

SN - 1532-4435

VL - 2

SP - 139

EP - 154

JO - Journal of Machine Learning Research

JF - Journal of Machine Learning Research

ER -