Optimising Prediction in Overlapping and Non-Overlapping Regions

Abstract

Researchers working on real world classification data have identified that a combination of class overlap with class imbalance and high dimensional data is a crucial problem and are important factors for degrading performance of the classifier. Hence, it has received significant attention in recent years. Misclassification often occurs in the overlapped region as there is no clear distinction between the class boundaries and the presence of high dimensional data with an imbalanced proportion poses an additional challenge. Only a few studies have ever been attempted to address all these issues simultaneously; therefore; a model is proposed which initially divides the data space into overlapped and non-overlapped regions using a K-means algorithm, then the classifier is allowed to learn from two data space regions separately and finally, the results are combined. The experiment is conducted using the Heart dataset selected from the Keel repository and results prove that the proposed model improves the efficiency of the classifier based on accuracy, kappa, precision, recall, f-measure, FNR, FPR, and time.