Primal Estimated Subgradient Solver for SVM for Imbalanced Classification
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We aim to demonstrate in experiments that our cost sensitive PEGASOS SVM balances achieve good performance on imbalanced data sets with a Majority to Minority Ratio ranging from 8.6 to one through 130 to one. We evaluate the performance by examining the learning curves. We will also examine the effect of varying the hyperparameters via validation curves. We compare our PEGASOS Cost-Sensitive SVM's results on three of the datasets Ding analyzed using his LINEAR SVM DECIDL method. We will use Python rather than MATLAB as python has dictionaries for storing mixed data types during multi-parameter cross-validation.
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