Feature selection for interval-valued data via FRIC-model

Abstract

Feature selection is one basic technology for data mining. This paper investigates feature selection for interval-valued data via fuzzy rough iterative computation model (FRIC-model). To depict the similarity between samples in an interval-valued decision information system (IVDIS), the fuzzy symmetry relation in an IVDIS is first introduced from the perspective of “The similarity between information values is fed back to the feature set”. After that, several attribute evaluation functions, such as fuzzy positive regions, dependency functions and attribute importance functions are defined. Subsequently, FRIC-model for interval-valued data is established by using the iterations of these functions. Next, An feature selection algorithm in an IVDIS based on this model is presented. Lastly, numerical experiments and statistics tests are carried out to estimate the performance of the presented algorithm. The experimental results illustrate that the presented algorithm maintains high classification accuracy, and does not occupy too much memory. These findings will provide new perspective for feature selection in an IVDIS.