Envelope Imbalance Learning Algorithm based on Multilayer Fuzzy C-means Clustering and Minimum Interlayer discrepancy

TL;DR

This paper introduces a novel deep learning-based oversampling algorithm that uses multilayer fuzzy c-means clustering and a discrepancy mechanism to improve classification on imbalanced datasets, outperforming existing methods.

Contribution

It proposes a multilayer fuzzy c-means clustering method combined with a minimum interlayer discrepancy mechanism for high-quality oversampling without prior knowledge.

Findings

Significantly outperforms 10+ popular algorithms on 33 datasets.

Effectively handles between-class and within-class imbalance issues.

Ensures high-quality balanced instances through deep instance envelope network.

Abstract

Imbalanced learning is important and challenging since the problem of the classification of imbalanced datasets is prevalent in machine learning and data mining fields. Sampling approaches are proposed to address this issue, and cluster-based oversampling methods have shown great potential as they aim to simultaneously tackle between-class and within-class imbalance issues. However, all existing clustering methods are based on a one-time approach. Due to the lack of a priori knowledge, improper setting of the number of clusters often exists, which leads to poor clustering performance. Besides, the existing methods are likely to generate noisy instances. To solve these problems, this paper proposes a deep instance envelope network-based imbalanced learning algorithm with the multilayer fuzzy c-means (MlFCM) and a minimum interlayer discrepancy mechanism based on the maximum mean discrepancy (MIDMD). This algorithm can guarantee high quality balanced instances using a deep instance envelope network in the absence of prior knowledge. In the experimental section, thirty-three popular public datasets are used for verification, and over ten representative algorithms are used for comparison. The experimental results show that the proposed approach significantly outperforms other popular methods.