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In the era of big data, data are being collected, stored, and analyzed at an unprecedented rate. Owing to the limitations of the quantity, diversity, and complexity of data, traditional data reduction techniques cannot effectively remove redundant attributes and reduce data uncertainty. In this paper, a bi-directional fuzzy granular-cabin parallel attribute reduction accelerator with granular-group collaboration is proposed. First, the in-memory computing technology of Spark is used to divide the dataset into different subsets to achieve distributed parallel acceleration. Second, a novel bi-directional fuzzy granular-cabin model is constructed using virtual samples, which greatly compresses the fuzzy neighborhood query space. Then, a granular-group collaboration attribute reduction method with the fusion of the proposed granular-cabin and attribute group is investigated, which uses the concept of similarity to divide attributes into different attribute groups, thus reducing the number of iterations for evaluating attributes. Finally, the reduction results of the sub-nodes are aggregated to the master-node, and the ranking results are evaluated and shown to improve the classification accuracy of attribute reduction. This paper presents experiments on eighteen public datasets, including six large-scale datasets. The experimental results show that the proposed method not only reduces the computational cost but also improves the classification accuracy of the reduced subset.
Ju et al. (Mon,) studied this question.