Weight Balanced Linear Programming SVM on Skewed Distribution and Its Evaluation

Support Vector Machines (SVM) have been shown to have strong classification capability and good generalization results. Among SVM's, linear-programming SVM's (LPSVM) have been shown to have better ability to adapt to large data sets with more efficiency. However, there have been several variations of LPSVM's, and their ability to tackle with skewed distributions are not investigated yet. In this research, we compared 4 LPSVM formulations and suggested one of them as the candidate for classifying skewed distributions. We also suggested a novel formula for evaluation of correctness of classification results, which is more suitable in multicategory cases. We also introduced a new concept of output balancing, which can be useful in recovering lost classes in k-SVM applications. Through the experiments, we showed that weight balancing approach is effective in multicategory classification.