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Erschienen in:
Human Action Recognition Using Trajectory-Based Spatiotemporal Descriptors Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

Text Document Classification with PCA and One-Class SVM

verfasst von : B. Shravan Kumar, Vadlamani Ravi

Erschienen in: Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Verlag: Springer Singapore

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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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Metadaten
Titel
Text Document Classification with PCA and One-Class SVM
verfasst von
B. Shravan Kumar
Vadlamani Ravi
Copyright-Jahr
2017
Verlag
Springer Singapore
Buch
Proceedings of the 5th International Conference on Frontiers in Intelligent Computing: Theory and Applications

Print ISBN: 978-981-10-3152-6

Electronic ISBN: 978-981-10-3153-3

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-981-10-3153-3_11

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