Dynamic Fuzzy Features Selection Based on Variable Weight

In this paper, a new scheme for dynamic fuzzy feature selection based on variable weight is proposed to optimize the fuzzy feature subset with the important features dynamically. Firstly, the sliding window is adopted to divide the fuzzy dataset. In the first sliding window, the off-line fuzzy features selection algorithm is proposed to access the candidate fuzzy feature subset by calculating the weight of each fuzzy input feature according to the mutual information between the fuzzy input features and the output feature. Based on this, the optimal fuzzy feature subset are obtained by combining the backward feature selection method with the fuzzy feature selection index. With the new sliding window, the on-line fuzzy features selection algorithm is proposed, by integrating the optimal fuzzy feature selection result in the previous sliding window with the candidate fuzzy feature set in the current sliding window, the importance of the fuzzy input feature is calculated to obtain the optimal feature subset in the current window. Finally, the evolving relationship of the fuzzy input features is found with the fuzzy feature weights between the sliding windows. The simulation results show that the proposed algorithm has a significant improvement in the adaptability and prediction accuracy.