Conscientious Ant Colony Optimization Based Support Vector Machine for Text Document Classification
Deepa A1, E. Chandra Blessie2
1A. Deepa*, Assistant Professor in the Department of MCA in Nehru College of Management, Coimbatore.
2Dr. E.Chandra Blessie, Professor in the Department of MCA in Nehru College of Management, Coimbatore.
Manuscript received on December 17, 2019. | Revised Manuscript received on December 23, 2019. | Manuscript published on January 10, 2020. | PP: 1056-1060 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8062019320/2020©BEIESP | DOI: 10.35940/ijitee.C8062.019320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Document classification indicates the keyword extraction and it become a thrust research in text mining research. The main purpose of keyword extraction is to classify the documents in a more efficient manner. Misclassification of documents may lead the results to worst case. Hence, there exists a need for optimization to precede the document classification more efficiently. In this paper Conscientious Ant Colony Optimization based Support Vector Machine is proposed to classify the documents. Different keyword extraction methods are available for extracting the contents from documents. Proposed classifier is ensemble with selected keyword extraction methods to increase the classification accuracy. Results shows that the proposed classifier has got better accuracy when ensemble with different keyword extraction methods. The results show that the proposed classifier has better performance in terms of Classification Accuracy and F-Measure, than baseline classifiers.
Keywords: Classification, ACM, Mining, NBA, Reuters, Text
Scope of the Article: Classification