Distance-based margin support vector machine for classification

Recently, the development of machine-learning techniques has provided an effective analysis tool for classification problems. Support vector machine (SVM) is one of the most popular supervised learning techniques. However, SVM may not effectively detect the instance of the minority class and obtain a lower classification performance in the overlap region when learning from complicated data sets. Complicated data sets with imbalanced and overlapping class distributions are common in most practical applications. Moreover, they negatively affect the classification performances of the SVM. The present study proposes the use of modified slack variables within the SVM (MS-SVM) to solve complex data problems, including class imbalance and overlapping. Artificial and UCI data sets are provided to evaluate the effectiveness of the MS-SVM model. Experimental results indicate that the MS-SVM performed better than the other methods in terms of accuracy, sensitivity, and specificity. In addition, the proposed MS-SVM is a robust approach for solving different levels of complex data sets.