Abstract
We propose a document classifier based on principal component analysis (PCA) and one-class support vector machine (OCSVM), where PCA helps achieve dimensionality reduction and OCSVM performs classification. Initially, PCA is invoked on the document-term matrix resulting in choosing the top few principal components. Later, OCSVM is trained on the records of the matrix corresponding to the negative class. Then, we tested the trained OCSVM with the records of the matrix corresponding to the positive class. The effectiveness of the proposed model is demonstrated on the popular datasets, viz., 20NG, malware, Syskill, & Webert, and customer feedbacks of a Bank. We observed that the hybrid yielded very high accuracies in all datasets.
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Shravan Kumar, B., Ravi, V. (2017). Text Document Classification with PCA and One-Class SVM. In: Satapathy, S., Bhateja, V., Udgata, S., Pattnaik, P. (eds) Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications . Advances in Intelligent Systems and Computing, vol 515. Springer, Singapore. https://doi.org/10.1007/978-981-10-3153-3_11
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DOI: https://doi.org/10.1007/978-981-10-3153-3_11
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