An Improved Attribute Reeducation Algorithm Based on Mutual Information for Non-Care Information System

Sumin Yang, Shaochong Feng, Jing Chen, Hongli Yuan


For an information system the attribute reduction is one key to choose some important indexes, but the core attribute is the foundation for the present heuristic attribute reduction algorithms of rough set theory based on discernibility matrix and mutual information theory. When we apply these algorithms on the non-core information system, there will be the following problems, such as too much calculation problem, excessive reduction, or insufficient reduction. According to the characteristics of non-core information system, we propose one new heuristic attribute reduction algorithm based on mutual information, in which the evaluation of attribute important degree depends on the increment of mutual information and information entropy. And the attribute with the largest attribute important degree is selected for the initial core attribute, by which the problem that the random selection attribute cause the computational complexity is solved. By the proposed algorithm we cannot only improve the efficiency of attribute reduction, but decrease the number of attribute reduction. Both the theoretic analysis and the simulation experiments verify that the proposed algorithm is validity


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