2-Tuple and Rough Set Based Reduction Model for Multi-sensory Evaluation Indicators

• Published in: 东华大学学报（英文版） Vol. 31; no. 1; pp. 50 - 56
• Main Author: XIA Ya-qin ZHOU Hong-lei ZHU Ru-peng
• Format: Journal Article
• Language: English
• Published: Fashion Institute, Donghua University, Shanghai 200051, China%Fashion Institute, Donghua University, Shanghai 200051, China%Key Laboratory of Precision and Micro-Manufacturing Technology, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China 2014; Key Laboratory of Precision and Micro-Manufacturing Technology, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
• Subjects: rough set; multi-sensory evaluation; indicator reduction; 2-tuple
• ISSN: 1672-5220

In order to lessen adverse influences of excessive evaluative indicators of the initial set in multi-sensory evaluation, a 2.tuple and rough set based reduction model is built to simplify the initial set of evaluative indicators. In the model, a great variety of descriptive forms of the multi-sensory evaluation are also taken into consideration. As a result, the method proves effective in reducing redundant indexes and minimizing index overlaps without compromising the integrity of the evaluation system. By applying the model in a multi-sensory evaluation involving community public information service facilities, the research shows that the results are satisfactory when using genetic algorithm optimized BP neural network as a calculation tool. It shows that using the reduced and simplified set of indicators has a better predication performance than the initial set, and 2-tuple and rough set based model offers an efficient way to reduce indicator redundancy and improves prediction capability of the evaluation model.