Support Vector Machine for Text Categorization using Principle Component Analysis in Data Mining
A. Ravi Kumar1, G. Anil Kumar2
1A. Ravi Kumar, Research scholar, SriSatya Sai University of Technology & Medical Sciences, Sehore, Bhopal, Madhya Pradesh, India.
2G.Anil Kumar, Principal, Scient Institute of Technology, Hyderabad, Telangana, India.
Manuscript received on January 01, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on January 30, 2020. | PP: 3164-3167 | Volume-8 Issue-5, January 2020. | Retrieval Number: D7350118419/2020©BEIESP | DOI: 10.35940/ijrte.D7350.018520
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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: Data mining is the withdrawal of concealed prescient information also obscure data, examples, connections and learning by investigating the enormous informational collections which are hard to discover and distinguish with customary measurable techniques. The major issues in text categorization are classification accuracy and computation time. To overcome these issues, an efficient classification method is needed for high differentiation exactness as fine as minimizing the computation period. In this work, we propose the classification of data using support vector machine for text categorization along with principle component analysis. Bolster Vector Machines is a managed learning system with numerous attractive characteristics that make it a prevalent calculation. Principle Component Analysis (PCA) is the feature removal technique is used towards mine the features with in the text. Chi-Square is a further assortment technique it is used to selecting the features from removed features. Finally by this proposed work, the classification accuracy also computation period is improved than other existing algorithms in many applications.
Keywords: Principle Component Analysis (PCA), Chi-Square.
Scope of the Article: Data Mining.