Generalizing fuzzy k-nearest neighbor classifier using an OWA operator with a RIM quantifier

Abstract

This paper proposes a new fuzzy k-nearest neighbor (FKNN) method, called the ordered weighted averaging (OWA) with regular increasing monotone quantifier-based fuzzy k-nearest neighbor (OWARIM-FKNN) classifier. The proposed method aims at enhancing the classification performance of the KNN rule-base variants, especially the local mean-based approaches, while dealing with outlier and data uncertainty issues. In the proposed method, the OWA operator is used to generalize the multi-local mean vectors from each class. The resulting $k$ multi-local OWA vectors are then used to create the class representative pseudo nearest neighbors. Lastly, the new sample is classified into the class with the highest membership degree measured using the weighted distance between the new sample and the pseudo nearest neighbor. The classification performance of the proposed method was examined using one artificial and twenty-seven real-world data sets compared with the results obtained from eight related KNN variants. Experimental results showed that the proposed OWARIM-FKNN classifier achieves the highest average accuracy of 87.59% with an average confidence interval of $\pm$0.64, outperforming all baseline methods. Using the Friedman and Nemenyi tests, the analysis further confirms that the proposed method shows statistically significant performance improvements.