Improving learning from the complex multi-class imbalanced and overlapped data by mapping into higher dimension using SVM++

Several issues are there to prevent the traditional classifiers from getting an acceptable performance level while learning from multi-class problems. One of the main problems is the unequal distribution of samples, which significantly reduces the efficiency of the underlying classifier when combined with incompatible optimization benchmarks and data overlapping phenomena. The classifier performance is compromised beyond the expected level by the combined effects of imbalanced distribution and sample overlapping around the class boundaries. This problem worsens with the increase in the number of classes in the multi-class scenario. Despite having a more significant combined effect on classifier performance, the combined effects of imbalanced data and overlapping questions have been given the least attention in the research. To improve models' learning from imbalanced multi-class and overlapping of shared attributes issues, this work introduces SVM++, a modified version of support vector machines (SVM). Comprising of three steps, Algorithm-1 finds and splits the training set into overlapping and non-overlapping samples. Algorithm-2 then separates the overlapped data into the Critical-1 and Critical-2 regions. The Critical-1 region consists of overlapped samples, sharing similar characteristics, which is the main cause of degraded classification performance. In the third step, an algorithm based on the mean of the maximum and minimum distance of the Critical-1 region samples is proposed by improving the traditional SVM kernel mapping function to a higher dimension. Thirty real datasets with various imbalances and degrees of overlap are utilized to compare our suggested algorithms' supremacy with the state-of-the-art classifiers.