Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 104, Issue -, Pages 148-165Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2018.11.003
Keywords
Attribute reduction; Cost-sensitive learning; (In)discernibility; Granular computing; Three-way decisions
Categories
Funding
- National Natural Science Foundation of China [41604114]
Ask authors/readers for more resources
In the research spectrum of rough set, the task of attribute reduction is obtaining a minimal attribute subset that preserves certain properties of the original data. Cost-sensitive attribute reduction aims at minimizing various types of costs. Approximate attribute reduction allows decision makers to leverage the advantages of knowledge discovery and their own preferences. This paper proposes the cost-sensitive approximate attribute reduction problem under both qualitative and quantitative criteria. The qualitative criterion refers to the indiscernibility, while the quantitative criterion refers to the approximate parameter s and the cost. We present a framework based on three-way decisions and discernibility matrix to handle this new problem. First, a quality function for attribute subsets is designed with the interpretation of a hierarchical granular structure. Second, a fitness function is designed for cost performance index by investigating attribute significance. Third, three-way decision theory is applied to partition the attributes into three groups based on the fitness function and a threshold pair (alpha, beta). Finally, deletion-based and addition based cost-sensitive approximate reduction algorithms are designed under this framework. Experimental results indicate that our algorithms outperform the state-of-the-art methods. (C) 2018 Elsevier Inc. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available