vioft2nntf2t|tblJournal|Abstract_paper|0xf4fff4ac1a00000073a8030001000500 In this paper, we studied the performances of models built using various SVM implementations during the multiclass classification task of automated evaluation of descriptive answers. The performances were evaluated on five datasets each with 900 samples and with each of the datasets treated using symmetric uncertainty feature selection filter. We quantitatively analyzed the best SVM implementation technique from amongst the 17 different SVM implementation combinations derived by using various SVM classifier libraries, SVM types and Kernel methods. Accuracy, F Score, Kappa and Area under ROC curve are used as model evaluation metrics in order to evaluate the models and rank them according to their performances. Based on the results, we derived the conclusion that SMO classifier when used with Polynomial kernel is the overall best performing classifier applicable for auto evaluation of descriptive answers.
C. Sunil Kumar1, R. J. Rama Sree2
Bharathiar University, India1, Rashtriya Sanskrit Vidyapeetha, India2
Descriptive Answers, Auto Evaluation, SVM, LibLINEAR, LibSVM, SMO, Kernels
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 5 , Issue: 3 , Pages: 971-978 )
Date of Publication :
April 2015
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