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International Journal of Statistika and Mathematika, ISSN: 2277- 2790 E-ISSN: 2249-8605
Volume 4, Issue 3, 2013 pp 74-80
Research Article
Comparison of Support Vector Machines and Linear Discriminant Analysis for Indian Industries
R. Madhanagopal1, R.C. Avinaash2 and K. Karthick3
{1Assistant Professor, 2, 3 Post Graduate Students} Department of Statistics, Madras Christian College, Chennai, Tamil Nadu, INDIA.
Academic Editor: Dr. Dase R.K.
Support vector machines (SVM) methods have become increasingly popular tools for data mining tasks viz., classification, regression and novelty detection. The present paper deals with classification of Indian industries using SVM. Industries stable for one month period in NIFTY was selected, of which 50 companies in NIFTY, 32 were found to be stable. Twenty eight key financial ratios of these companies were taken for a period of five financial years (April 2007 to March 2012). Fuzzy clustering and SVM were used to explore the financial data. Principal component analysis (PCA) was applied and it reduced the twenty eight financial ratios into seven components. Thereafter, fuzzy clustering was performed on scores of PCA and was formed into two groups which were categorized into high and low performing industries based on their mean values. SVM was used as a classifier of the industries and it was compared with well known and old classification technique, Linear discriminant analysis (LDA). The classification accuracy in training and testing data set for SVM was 97.32% and 100 % whereas for LDA it was 87.29 and 93.75% respectively.Therefore, the present study concludes that SVM performed better than LDA in the classification of industries.
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