Rule Extraction Model Based on Decision Dependency Degree

Rule extraction is the core in rough set. Two procedures are contained in rule extraction: one is attribute reduction and another is attribute value reduction. It was proved through computational complexity perspective that obtaining all the reduction, minimum attribute reduction, and minimum attrib...

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Bibliographic Details
Published in:Mathematical problems in engineering 2019, Vol.2019 (2019), p.1-16
Main Authors: Chen, Xinying, Sun, Yunhao, Li, Guanyu
Format: Article
Language:English
Subjects:
Algorithms
Complexity
Dependence
Heuristic methods
Reduction
Redundancy
Reproduction (copying)
Rough set models
System effectiveness
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Summary:Rule extraction is the core in rough set. Two procedures are contained in rule extraction: one is attribute reduction and another is attribute value reduction. It was proved through computational complexity perspective that obtaining all the reduction, minimum attribute reduction, and minimum attribute value reduction is an NP problem. So, generally, a heuristic reduction method is used to solve attribute reduction and attribute value reduction. However, for most heuristic methods, it is hard to put into practice and has high cost on computational complexity. Moreover, part of the methods extracted redundant rules. To approach a quick and effective model for rule extraction in decision systems, against the concept of distinguishable relation, relevant concepts and basic theorems of rule extraction are proposed. In order to get concise and accurate rules quickly, algorithms for finding conflict object set, finding duplicate object set, and finding redundant rules are given. After that, using decision dependency degree as attribute importance to determine the importance of each attribute in rule object, a new rule extraction model based on decision dependency degree is proposed in this paper. Compared with the previous models, this model does not generate matrix; instead, it finds conflict object set and duplicate object set by equivalence class, and consequently, improves the time performance to maxOCU, OC2U/C, and OREDU/C/RED2. The theoretical analysis and experimental research show that the new model more accurately and effectively reduces the redundant data and extracts more concise decision rules from dataset.
ISSN:1024-123X
1563-5147
DOI:10.1155/2019/5850410