AFNFS: Adaptive fuzzy neighborhood-based feature selection with adaptive synthetic over-sampling for imbalanced data

The classification efficiency of majority classes for imbalanced data is so concerned in real -world applications. Almost fuzzy neighborhood radius still needs to be manually set and many entropy measures may ignore the boundary region of data, these limitations will result in the poor classification effect. To address these limitations, this paper designs a novel adaptive fuzzy neighborhood-based feature selection method for imbalanced data with adaptive synthetic over-sampling. First, the closeness is defined according to the vari-ance distance between the samples of the minority class, the pair set of neighboring sam-ples is designed, and then an improved adaptive synthetic over-sampling model is presented for constructing balanced decision systems consisting of the synthetic samples and original samples. Second, an adaptive fuzzy neighborhood radius is developed when using the data margins of all homogeneous and heterogeneous samples. Then the adaptive fuzzy neighborhood granule and upper and lower approximations are defined to construct a new FNRS model. Thus, approximate accuracy and roughness are presented to measure the uncertainty from the fuzzy and rough perspectives for imbalanced data. Third, by com-bining the roughness with adaptive fuzzy neighborhood entropy, adaptive fuzzy neighbor-hood joint entropy is constructed to evaluate the uncertainty in fuzzy neighborhood decision systems from two viewpoints of algebra and information. Then the reduced set and the significance of the feature are further developed. Finally, this improved adaptive synthetic over-sampling algorithm is designed to aim to build this balanced decision sys-tem, and an adaptive fuzzy neighborhood-based feature selection algorithm with the tol-erance parameter is developed to achieve an optimal feature subset. Experiments on 26 imbalanced datasets demonstrate that the constructed algorithms compared to the other related algorithms are effective.(c) 2022 Elsevier Inc. All rights reserved.