Superensemble classifier for improving predictions in imbalanced datasets
Tanujit Chakraborty, Ashis Kumar Chakraborty · Communications in Statistics Case Studies Data Analysis and Applications · 2020
Learning from an imbalanced data set presents a tricky problem in which traditional learning algorithms perform poorly. Traditional classifiers usually aim to optimize the overall accuracy without considering the relative distribution of each class. To improve predictions in imbalanced classification problems, this article presents a superensemble classifier that maps Hellinger Distance Decision Trees (HDDT) into Radial Basis Function Networks (RBFN). Regularity conditions for universal consistency and the idea of parameter optimization of the proposed model are given in this article. The proposed distribution-free model can be applied for feature selection cum imbalanced classification problems. We have also provided enough experimental evidence using various real-life data sets to assess the performance of the proposed model. Its effectiveness and competitiveness concerning different state-of-the-art models are shown.