A representation coefficient-based k-nearest centroid neighbor classifier. (15th May 2022)

• Record Type: Journal Article
• Authors: Gou, Jianping; Sun, Liyuan; Du, Lan; Ma, Hongxing; Xiong, Taisong; Ou, Weihua; Zhan, Yongzhao
• Is Part Of: Expert systems with applications. Volume 194(2022)
• Publication Date: 2022-05-15
• Subjects: K-nearest neighbor rule -- Nearest centroid neighborhood -- K-nearest centroid neighbor rule -- Pattern recognition; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.116529
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: K -nearest neighbor rule (KNN) has been regarded as one of the top 10 methods in the field of data mining. Due to its simplicity and effectiveness, it has been widely studied and applied to various classification tasks. In this article, we develop a novel representation coefficient-based k -nearest centroid neighbor method (RCKNCN), which aims to further improve the classification performance and reduce the method's sensitivity to the neighborhood size k, especially in the cases of small sample size. Different from existing KNN-based methods, RCKNCN is able to capture both the proximity and the geometry of k -nearest neighbors, and learn to differentiate the contribution of each neighbor to the classification of a testing sample through a linear representation method. Moreover, under the RCKNCN framework, we also propose a novel weighted majority voting algorithm using the representation coefficients associated with individual nearest centroid neighbors, which are deemed to hold more discriminative information of the neighbors. To fully study the classification performance of RCKNCN, we compare it with the state-of-the-art KNN-based methods on many data sets that are widely used in the literature. The extensive experiments demonstrate the effectiveness and robustness of our method in various classification tasks. Highlights: Propose RCKNCN based on both NCN and representation of neighbors. Differentiate the contribution of each centroid neighbor via representation. Design a new representation coefficient-based majority voting decision.