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

• URL: http://arxiv.org/abs/2212.03182v1
• Date: Wed, 30 Nov 2022 01:49:06 GMT
• Title: Overlapping oriented imbalanced ensemble learning method based on projective clustering and stagewise hybrid sampling
• Authors: Fan Li, Bo Wang, Pin Wang, Yongming Li
• Abstract summary: This paper proposes an ensemble learning algorithm based on dual clustering and stage-wise hybrid sampling (DCSHS) 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.
• Score: 22.32930261633615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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