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Authors
Affiliations
1 Department of Computer Science, Bishop Heber College, IN
Source
ICTACT Journal on Soft Computing, Vol 6, No 3 (2016), Pagination: 1231-1234
Abstract
Automatic text classification is a prominent research topic in text mining. The text pre-processing is a major role in text classifier. The efficiency of pre-processing techniques is increasing the performance of text classifier. In this paper, we are implementing ECAS stemmer, Efficient Instance Selection and Pre-computed Kernel Support Vector Machine for text classification using recent research articles. We are using better pre-processing techniques such as ECAS stemmer to find ischolar_main word, Efficient Instance Selection for dimensionality reduction of text data and Pre-computed Kernel Support Vector Machine for classification of selected instances. In this experiments were performed on 750 research articles with three classes such as engineering article, medical articles and educational articles. The EIS-SVM classifier provides better performance in real-time research articles classification.
Keywords
Automatic Text Classification, ECAS Stemmer, Efficient Instance Selection and Pre-Computed Kernel Support Vector Machine.
