Fusing entropy measures for dynamic feature selection in incomplete approximation spaces

摘要

The selection of informative and discriminative features from incomplete dynamic data has emerged as an essential problem because the data preparation in modern applications is a dynamic updating process, and the collected data are often fraught with missing or unobserved values. Using the effectiveness of entropy measures to quantify uncertainty information in incomplete approximation spaces under the framework of tolerance rough sets, a novel incremental feature selection approach is presented in this study from an information-theoretic perspective. Based on the updating patterns of tolerance classification and decision partition induced by conditional and decision features, a novel fused representation of entropy measures in an incomplete approximation space is proposed to accelerate the calculation of feature significance during the heuristic search process. A computationally efficient feature selection algorithm is developed by integrating the incremental fusing strategy of entropy when the data increases dynamically in size. Numerical experiments were performed on several benchmark datasets with data updating for different data sizes and incremental ratios to demonstrate the effectiveness of our method. The superiority of the proposed method is established extensively in terms of computational efficiency, cardinality and accuracy of the selected feature subset.