Attribute Reduction Algorithm on Balancing Profit and Risk

2013 ◽  
Vol 411-414 ◽  
pp. 1919-1922 ◽  
Author(s):  
De Xing Wang ◽  
Jie Long Xu ◽  
Yun Zhang
Keyword(s):  
Decision Making ◽  
Heuristic Search ◽  
Social Life ◽  
Search Algorithm ◽  
Attribute Reduction ◽  
Theoretic Model ◽  
Minimal Risk ◽  
Economic Activities ◽  
Heuristic Search Algorithm ◽  
Maximal Profit

Usually it is taken grant that we achieve the maximal profit and the minimal risk in industry, agriculture, economic activities and social life. It is an important problem in a decision-making process on how to balance profit and risk and find out practical decision-making ways. This paper builds a decision-theoretic model which can balance profit and risk and provide a heuristic search algorithm of the attribute reduction. This algorithm takes the profit and cost as the heuristic function and outputs an optimal attribute set. At last, the example shows that the proposed algorithm is correct and efficient.

scholarly journals Positive Macroscopic Approximation for Fast Attribute Reduction

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Zheng-Cai Lu ◽  
Zheng Qin ◽  
Qiao Jing ◽  
Lai-Xiang Shan
Keyword(s):  
Heuristic Search ◽  
Search Algorithm ◽  
Attribute Reduction ◽  
Boundary Region ◽  
Feature Subset ◽  
Decision Systems ◽  
Heuristic Search Algorithms ◽  
Heuristic Search Algorithm ◽  
Single Target ◽  
Lower And Upper Approximations

Attribute reduction is one of the challenging problems facing the effective application of computational intelligence technology for artificial intelligence. Its task is to eliminate dispensable attributes and search for a feature subset that possesses the same classification capacity as that of the original attribute set. To accomplish efficient attribute reduction, many heuristic search algorithms have been developed. Most of them are based on the model that the approximation of all the target concepts associated with a decision system is dividable into that of a single target concept represented by a pair of definable concepts known as lower and upper approximations. This paper proposes a novel model called macroscopic approximation, considering all the target concepts as an indivisible whole to be approximated by rough set boundary region derived from inconsistent tolerance blocks, as well as an efficient approximation framework called positive macroscopic approximation (PMA), addressing macroscopic approximations with respect to a series of attribute subsets. Based on PMA, a fast heuristic search algorithm for attribute reduction in incomplete decision systems is designed and achieves obviously better computational efficiency than other available algorithms, which is also demonstrated by the experimental results.

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