Approach for incomplete fuzzy hybrid decision system on neighborhood rough set

In order to reduce the incomplete fuzzy hybrid decision systems, a generalized neighborhood rough set model is proposed. Discrimination methods of the missing values and a hybrid reduction algorithm are also developed. The model approximates an arbitrary subset in the universe with neighborhood granules, and the generalized neighborhood relations are the generalization of the asymmetric similarity relations, the tolerance relations and the fuzzy equivalence relations. The model can deal with the incomplete fuzzy hybrid decision system directly. The discrimination methods of the lost and the unrelated conditions are developed based on the assumption of the consistency classification. The influence of the noise samples and the neighborhood values on the classification accuracy is investigated. The validity and feasibility of the model and the reduction algorithm are demonstrated by experiments on HitSHT and two UCI machine learning databases.