Extended rough set model based on modified data-driven valued tolerance relation

Classical rough set theory is based on the conventional indiscernibility relation. It is not suitable for analyzing incomplete information. Some successful extended rough set models based on different non-equivalence relations have been proposed. The data-driven valued tolerance relation is such a non-equivalence relation. However, when predicting the unknown attribute value of an object, it regards the frequency of an attribute value approximately as the probability of appearance of this value, without considering the effects of other known attribute values of this object on predicting the unknown attribute value. In this paper, considering both the frequency of the known attribute values and the influence weight to predict the unknown attribute values. Modified data-driven valued tolerance relation (MDVT) is defined. On this basis, an extended rough set model based on modified data-driven valued tolerance relation is proposed. Some properties of the new model are analyzed. Experimental results show that the MDVT can get better classification results than other generalized indiscernibility relations.