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

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.