Feature Subset Selection for Multi-scale Neighborhood Decision Information System
ZHANG Lujing1,2, LIN Guoping1,2, LIN Yidong1, KOU Yi1
1. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000; 2. Fujian Key Laboratory of Granular Computing and Applications, Minnan Normal University, Zhangzhou 363000
Abstract Feature subset selection for multi-scale decision information system is an effective data preprocessing method for multi-scale classification problems. However, data types are often diverse and mixed in real application. The existing multi-scale models cannot handle these data effectively. To solve this problem, a formal definition of multi-scale neighborhood radius for multi-source heterogeneous multi-scale data is proposed in this paper. Multi-scale neighborhood information granule is constructed and its related properties are studied. Attribute significance is discussed, and a feature subset selection algorithm is proposed. Optimal scale selection and feature selection are conducted synchronously. By improving the Wu-Leung model, the scope of its application in practical problems is expanded to some extent. Finally, the feasibility and effectiveness of the proposed model and algorithm are verified on UCI datasets.
Fund:National Natural Science Foundation of China(No.11871259,12101289,12201284), Natural Science Foundation of Fujian Province(No.2021J01983,2021J01979)
Corresponding Authors:
LIN Guoping, Ph.D., professor. Her research interests include granular computing and artificial intelligence.
About author:: ZHANG Lujing, master student. Her research interests include granular computing and rough set theory.LIN Yidong, Ph.D., associate professor. His research interests include uncertain theories and application.KOU Yi, master student. His research interests include granular computing and machine learning.
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2023, Vol. 36 
