实验室硕士生蔡江河，邓玉辉老师等人联合撰写的论文《FIG: Feature-Weighted Information Granules With High Consistency Rate》被大数据领域的权威国际学术期刊《IEEE Transactions on Big Data》录用。论文将于2024年正式发表。 论文摘要如下： Abstract—Information granules are effective in revealing the structure of data. Therefore, it is a common practice in data mining to use information granules for classifying datasets. In the existing granular classifiers, the information granules are often classified according to the standard membership function only without considering the influence of different feature weights on the quality of granules and label classification results. In this article, we utilize the feature weighting of data to produce the information granules with high consistency rate called FIG. Firstly, we use consistency rate and contribution scores to generate information granules. Then, we propose a granular two-stage classifier GTC based on FIG. GTC divides the data into fuzzy and fixed points and then calculates the interval matching degree to assign data points to the most suitable cluster in the second step. Finally, we compare FIG with two state-of-the-art granular models, T-GrM and FGC-rule, and classification accuracy is also compared with other granular classification algorithms (FGA, HCMP-SVM). The extensive experiments on synthetic datasets and public datasets from UCI show that FIG has sufficient performance to describe the data structure and excellent capability under the constructed granular classifier GTC. Compared with T-GrM and FGC-rule, the time overhead required for FIG to obtain information granules is reduced by an average of 13%, the per unit quality of the granules is also increased by more than 2%, and an average of 9% improves GTC accuracy.