NOVEL INCREMENTAL ALGORITHMS FOR ATTRIBUTE REDUCTION FROM DYNAMIC DECISION TABLES WHEN ADDING OBJECT S

Tóm tắt

In recent years, attribute reduction methods following fuzzy rough set approach
have attracted the attention of researchers because they improve the accuracy of the
classification model. However, most of the proposed methods are performed on the unchanged
decision table. In this paper, we build an incremental algorithm to find the approximate reduct
according to the combined filter-wrapper approach. Experimental results on a number of
sample datasets show that the proposed incremental algorithm is more efficient than some
other incremental algorithms following the filter approach in terms of the number of reductive
set attributes and classification accuracy.