Về một thuật toán gia tăng tìm tập rút gọn trên bảng quyết định khi loại bỏ tập đối tượng
A Novel Incremental Algorithm for Finding the Reduct on the Decision Table when Deleting the Object Se
Abstract
Feature selection or attribute reduction for decision information systems has long been considered a key and indispensable problem in data mining and analysis. Some approaches based on rough set theory and extensions have brought many attribute reduction methods with impressive efficiency. However, up to now, some attribute reduction methods according to intuitionistic fuzzy sets have not received much interest. The advance of this approach is the ability to improve classification performance on noisy and inconsistent decision tables. This paper starts from a distance measure between two intuitionistic fuzzy partitions and then proposes an effective attribute reduction algorithm. Specifically, we first design an attribute reduction algorithm on the decision table without change. Next, we construct an incremental algorithm to process the decision table when deleting an object set. Some experimental results have shown that our proposed methods have superior performance to methods based on the rough set and fuzzy set in terms of the size of the reduct and the classification efficiency.
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