A new instance density-based synthetic minority oversampling method for imbalanced classification problems

Abstract

Many useful data pre-processing techniques have been developed to improve the performance of imbalanced classification problems. However, the results from the adoption of the several pre-processing methods indicated that the class imbalance is not the only reason for difficulties in learning in such a situation but the pre-processing for specific minority classes can improve classification performance more effectively. Thus, in this research, we present a new synthetic minority oversampling method based on instance density to enhance the performance of imbalanced classification problems. For this, two dimensional reduction techniques, i.e., principal component analysis (PCA) and factor analysis of mixed data (FAMD), are used to map the instances and k-nearest neighbor (kNN) method is used to classify minority class instances into four distinct types. Finally, through the comprehensive experiments on several imbalance datasets with different combination of subcategories, it is shown that the proposed method outperform the traditional oversampling methods.