An integrated approach of feature selection and parameter optimisation of kernel to enhance the performance of support vector machine
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Date
2015
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Abstract
Mining the big data is a challenging task due to the size of the
databases and the comple.xity in maintaining precise and non-redundant data.
Classification algorithms need to analyse hundreds of independent features in
these high dimensional databases for effective prediction. The performance of
classification algorithms could be enhanced data if irrelevant and redundant
data are removed. Feature selection algorithms help in identifying prominent
features that could enhance the performance of the classifier. Additionally, the
classification performance of support vector machine (SVM) could be
enhanced by setting appropriate kernel parameters. The kernel parameters of
SVM are tuned for each feature subset generated by feature selection and the
performance is analysed. The feature subset that enhances the classification
performance of SVM is the optimal feature subset of the dataset. Experiments
are done on three medical datasets. The empirical results prove that integrating
feature selection and optimising the kernel parameters enhance the performance
of the SVM classifier. The approach is validated in terms of increase in
accuracy and area under receiver operating characteristic (AUC) of the
classifier.