A NEW TYPE OF ATTRIBUTE REDUCTION FOR INCONSISTENT DECISION TABLES AND ITS COMPUTATION
YE Dong-yi, Zhaojiong Chen · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 2010
We introduce in this paper a new type of extended attribute reduction called M-reducts for an inconsistent decision table, which is defined to preserve the membership degree to a maximum decision class for each object of the table. It is shown that a M-reduct can actually preserve more decision information than it does by definition, including the maximum decision class itself and all deterministic decision information. Compared with other types of extended attribute reductions, the proposed type of attribute reduction is a better trade-off between the knowledge preserving capability and reduction efficiency. Illustrative examples are given and an effective algorithm for computing a M-reduct based on two summation functions of attribute sets is proposed together with its complexity analysis.