Superensemble Classifier for Improving Predictions in Imbalanced Datasets

TL;DR

This paper introduces a superensemble classifier that combines Hellinger distance decision trees with RBF networks to improve prediction accuracy on imbalanced datasets, addressing the bias towards majority classes.

Contribution

It proposes a novel distribution-free superensemble model integrating HDDT and RBFN, with theoretical consistency and parameter optimization, for better imbalanced classification performance.

Findings

Outperforms existing models on real-world datasets

Demonstrates effectiveness in feature selection and classification

Shows competitive results against state-of-the-art methods

Abstract

Learning from an imbalanced dataset is a tricky proposition. Because these datasets are biased towards one class, most existing classifiers tend not to perform well on minority class examples. Conventional classifiers usually aim to optimize the overall accuracy without considering the relative distribution of each class. This article presents a superensemble classifier, to tackle and improve predictions in imbalanced classification problems, that maps Hellinger distance decision trees (HDDT) into radial basis function network (RBFN) framework. Regularity conditions for universal consistency and the idea of parameter optimization of the proposed model are provided. The proposed distribution-free model can be applied for feature selection cum imbalanced classification problems. We have also provided enough numerical evidence using various real-life data sets to assess the performance of the proposed model. Its effectiveness and competitiveness with respect to different state-of-the-art models are shown.