Special Issue Article Open Access

Fisher Score Dimensionality Reduction for Svm Classification

Abstract

The Support Vector Machine is a discriminative classifier which has achieved impressive results in several tasks. Classification accuracy is one of the metric to evaluate the performance of the method. However, the SVM training and testing times increases with increasing the amounts of data in the dataset. One well known approach to reduce computational expenses of SVM is the dimensionality reduction. Most of the real time data are non- linear. In this paper, F- score analysis is used for performing dimensionality reduction for non – linear data efficiently. F- score analysis is done for datasets of insurance Bench Mark Dataset, Spam dataset, and cancer dataset. The classification Accuracy is evaluated by using confusion matrix. The result shows the improvement in the performance by increasing the accuracy of the classification.

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