Generalizing fuzzy k-nearest neighbor classifier using an OWA operator with a RIM quantifier
Mahinda Mailagaha Kumbure, Pasi Luukka · Expert Systems with Applications · 2025
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 ± 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. • A new variant of FKNN method using an OWA operator and a RIM quantifier is proposed. • Nearest neighbors-based multi-local OWA vectors are introduced in the FKNN method. • The new classifier is more robust to outlier effects than the state-of-the-art methods used. • Results highlight the effectiveness of the proposed approach for classification problems.