Attribute reduction is widely employed to improve the efficiency and accuracy of data analysis by eliminating redundant and irrelevant attributes from datasets. However, with the emergence of growing big data , the sequential execution of such algorithms becomes time-consuming and requires distributed computing capabilities to achieve scalable parallelization . This study proposes a novel attribute reduction algorithm for neighborhood decision systems. We introduce two novel metrics—the neighborhood evidential conflict degree (NECD) and neighborhood evidential conflict rate (NECR)—to compute heterogeneity between samples in the neighborhood and assess the significance of attributes in the feature space , respectively. These metrics assess the quality and selection of attribute subsets in attribute reduction, improving classification accuracy and computational efficiency. We also develop a sequentially forward selection attribute reduction method to select a feature subset through the defined NECR. Finally, we develop a distributed attribute reduction algorithm implemented in Apache Spark . Our approach involves a two-phase Map-Reduce process for K -Nearest Neighbors search, evidence combination, and NECR computation . NECR, as a measure of feature subset quality, enhances the feature subset's decision approximation capability of the data. Experimental results on small and large datasets demonstrate that the proposed algorithm outperforms benchmarking algorithms regarding classification accuracy and computational efficiency.

Transformer interpretability research is a hot topic in the area of deep learning. Traditional interpretation methods mostly use the final layer output of the Transformer encoder as masks to generate an explanation map. However, These approaches overlook two crucial aspects. At the coarse-grained level, the mask may contain uncertain information, including unreliable and incomplete object location data; at the fine-grained level, there is information loss on the mask, resulting in spatial noise and detail loss. To address these issues, in this paper, we propose a two-stage coarse-to-fine strategy (C2F-Explainer) for improving Transformer interpretability. Specifically, we first design a sequential three-way mask (S3WM) module to handle the problem of uncertain information at the coarse-grained level. This module uses sequential three-way decisions to process the mask, preventing uncertain information on the mask from impacting the interpretation results, thus obtaining coarse-grained interpretation results with accurate position. Second, to further reduce the impact of information loss at the fine-grained level, we devised an attention fusion (AF) module inspired by the fact that self-attention can capture global semantic information, AF aggregates the attention matrix to generate a cross-layer relation matrix, which is then used to optimize detailed information on the interpretation results and produce fine-grained interpretation results with clear and complete edges. Experimental results show that the proposed C2F-Explainer has good interpretation results on both natural and medical image datasets, and the mIoU is improved by 2.08% on the PASCAL VOC 2012 dataset.