Overlapping oriented imbalanced ensemble learning method based on projective clustering and stagewise hybrid sampling

TL;DR

This paper introduces a novel ensemble learning method called DCSHS that effectively addresses class imbalance and overlapping issues by combining dual clustering, stage-wise hybrid sampling, and transfer mapping, leading to superior performance on multiple datasets.

Contribution

The paper proposes a new ensemble learning algorithm that integrates projection clustering, hybrid sampling, and transfer learning to better handle class overlapping and imbalance.

Findings

DCSHS outperforms over ten algorithms on 30+ datasets.

The method effectively reduces class overlapping while maintaining class balance.

Experimental results demonstrate significant improvements across various evaluation metrics.

Abstract

The challenge of imbalanced learning lies not only in class imbalance problem, but also in the class overlapping problem which is complex. However, most of the existing algorithms mainly focus on the former. The limitation prevents the existing methods from breaking through. To address this limitation, this paper proposes an ensemble learning algorithm based on dual clustering and stage-wise hybrid sampling (DCSHS). The DCSHS has three parts. Firstly, we design a projection clustering combination framework (PCC) guided by Davies-Bouldin clustering effectiveness index (DBI), which is used to obtain high-quality clusters and combine them to obtain a set of cross-complete subsets (CCS) with balanced class and low overlapping. Secondly, according to the characteristics of subset classes, a stage-wise hybrid sampling algorithm is designed to realize the de-overlapping and balancing of subsets. Finally, a projective clustering transfer mapping mechanism (CTM) is constructed for all processed subsets by means of transfer learning, thereby reducing class overlapping and explore structure information of samples. The major advantage of our algorithm is that it can exploit the intersectionality of the CCS to realize the soft elimination of overlapping majority samples, and learn as much information of overlapping samples as possible, thereby enhancing the class overlapping while class balancing. In the experimental section, more than 30 public datasets and over ten representative algorithms are chosen for verification. The experimental results show that the DCSHS is significantly best in terms of various evaluation criteria.