Dynamic Fuzzy Rough Feature Selection Algorithm

Abstract

Since data update over time and space constantly, many rough set based incremental techniques have been proposed. Whereas there is less work on fuzzy rough set based feature selection (i.e., attribute reduction) from the dynamic data, especially the continuous dynamic data. In order to address this problem, an incremental attribute reduction algorithm based on fuzzy rough set is proposed for continuous data. First, some incremental mechanisms on fuzzy rough set are proposed, such as the incremental mechanisms of fuzzy positive region. Only some instances have insufficient identification capabilities on existing attribute reduction. That is, for the fuzzy positive region, there exists a key instance set. The incremental reduction algorithm updates the reduction results on the existing data by only considering the instances in the key instance set, but not the entire universe. Therefore, the incremental algorithm can quickly obtain a reduction update on dynamic data. Finally, some numerical experiments demonstrate that the incremental algorithm is effective and efficient compared to non-incremental attribute reduction algorithms.The incremental algorithm can save computing time greatly, especially on the datasets with high dimension.

Keywords

• feature selection
• incremental learning
• fuzzy rough set
• dependency function