A new nearest neighbor classification method based on fuzzy set theory and aggregation operators

The Fuzzy Nearest Neighbor Classification (FuzzyNNC) has been successfully used, as a tool to deal with supervised classification problems. It has significantly increased the classification accuracy by considering the uncertainty associated with the class labels of the training patterns. Nevertheless, FuzzyNNC's limited methods fail to efficiently handle the imprecision in features measurement and the uncertainty induced by the choice of the distance measure and the number of neighbors in the decision rule. In this paper, we propose a new method called Fuzzy Analogy-based Classification (FABC) to tackle the FuzzyNNC limitations. In this work, we exploit the fuzzy linguistic modeling and approximate reasoning materials in order to endow FABC with intelligent capabilities, like imprecision tolerance, optimization, adaptability and trade-off. Hence, our approach is composed of two main steps. Firstly, we describe the domain features using fuzzy linguistic variables. Secondly, we define the classification process using two intelligent aggregation operators. The first one allows the optimization of the similarity evaluation, by defining the adequate features to be considered. The second one integrates a trade-off strategy within the decision rule, by using a global voting approach with compensation property. The integration of such mechanisms will increase the classification accuracy and make the FuzzyNNC approach more useful for classification problems where imprecision and uncertainty are unavoidable. The proposed FABC is validated on the most known datasets, representing various classification difficulties and compared to the many extensions of the FuzzyNNC approach. The results obtained show that our proposed FABC method can be adapted to different classification problems and improve the classification accuracy. Thus, the FABC has the best rank value against the comparison methods with high significant level. Moreover, we conclude that our optimized similarity and global voting rule are more robust to handle the uncertainty in the classification process than those used by the comparison methods.