A Hypersphere Information Granule-Based Fuzzy Classifier Embedded With Fuzzy Cognitive Maps for Classification of Imbalanced Data
Rui Yin, Wei Lu, Jianhua Yang
Abstract
In this article, a hypersphere information granule-based fuzzy classifier integrated with Fuzzy Cognitive Maps (FCM), named FCM-IGFC, is proposed for the classification of imbalanced data. The proposed FCM-IGFC is structured by sequentially linking a Takagi-Sugeno-Kang (TSK) fuzzy system (FCM-TSK) – which is embedded with FCMs – and a rule-based fuzzy system (IGFS) based on hypersphere information granules. The FCM-TSK leverages the inference capabilities of the FCM to allow for the movement of data within the original space, while the IG-FS creates a mapping between samples and majority and minority classes using information granules. In this study, we introduced an innovative bottom-up granulation method and an overlap elimination technique for constructing hypersphere information granules. These methods facilitate the creation of information granules that accurately represent the structure of classes, even when dealing with im- balanced data. Moreover, the stacked structure of the FCM-IGFC offers data transfer capabilities. This helps reduce the complexity of distributions such as small, disjointed clusters and irregular class boundaries, with the support of the FCM, thereby making it easier to use information granules to describe the class structure. A series of experiments conducted on 12 publicly available datasets demonstrated that the performance of FCM-IGFC significantly surpasses that of existing granule-based fuzzy classifiers. Additionally, it is competitive with top-tier classifiers that incorporate sampling methods.