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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol.5(5): 379-383 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.537

Research on Combination of Rough Set and Seeking for Multi-objective Decision-Making

Zhenghua Cui, Jian Huang, Jianguo Hao, Mingguang Gao, and Jiangtao Kong
Abstract—Considering the technical indicator identification problem in preliminary weaponry research, the paper provides a novel approach by combining operational effectiveness with multi-objective optimization algorithms (MOO). Through using effectiveness evaluation approaches based on Rough Set Theory and having the set of 20 kinds of gunships, which are widely equipped nowadays, as the knowledge base, the paper first evaluates several performance factors and reaches the decision schema satisfying completion requirement after two times of filtering. Then the result is optimized using MOO algorithms under different weights over objectives. During the process, two phrases of filtering are applied, thus reducing the problem complexity. Meanwhile, the function of evaluating is also modified to improve the intuition and accuracy of results. This paper realizes the combinatory application of fuzzy set theory and MOO in the domain of operational effectiveness evaluation.

Index Terms—Rough sets, fuzzy multi-objective, configuration schemes, evaluate, decision-making.

Zhenghua Cui, Jian Huang, Jianguo Hao, and Jiangtao Kong are with the National University of Defense Technology, Changsha, Hunan, China (e-mail: cocolovelife@outlook.com, Huang_jian@139.com, hla2000@tom.com, Jiangtao_Kong@163.com).
Mingguang Gao is with the Beijing Institute of Technology, Beijing, China (e-mail: sunnyangelgmg@outlook.com).

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Cite: Zhenghua Cui, Jian Huang, Jianguo Hao, Mingguang Gao, and Jiangtao Kong, "Research on Combination of Rough Set and Seeking for Multi-objective Decision-Making," International Journal of Machine Learning and Computing vol.5, no. 5, pp. 379-383, 2015.

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