A Novel Approach to Heterogeneous Multi-class SVM Classification

The usability of multi-class support vector machine for processing and classifying heterogeneous data has defined in proposed study. Multi-class approaches are machine learning algorithms that construct a new subset of learning classifiers and then classify new models by adding the results of the base classifiers in various ways (characteristically by weighted or un-weighted voting). To construct the multi-class support vector machine classifier into subsets, first we have to decide different kernel tricks for getting better results. However, processing heterogeneous data by support vector machine based on different kernel functions have rarely used. The training time, cross-validation, classification accuracy of single class and multi-class are evaluated here. The sum rule for combining the subsets of multi-class classifiers has been used in this study. The experimental results analysis shows that our proposed multi-class approach achieves highest accuracy with heterogeneous data in comparison with other methods.

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