New fuzzy K-nearest neighbor algorithms for classification performance improvement

Document Type

Article

Source of Publication

Future Generation Computer Systems

Publication Date

3-1-2026

Abstract

In fuzzy k-nearest neighbor, smooth class boundaries are provided by each instance’s fuzzy degree of membership. However, there are additional costs associated with calculating the memberships due to memory limitations and runtime overhead. Furthermore, in the presence of class imbalance and outliers, the effectiveness and efficiency of the most advanced fuzzy kNNs continue to decline. Thus, new fuzzy kNNs with straightforward designs are developed in this study to substantially lessen the influence of these problems and improve overall performance. The local mean vectors with the single linkage and the cumulative means of neighbors are combined, establishing these models, which are referred to as LMSL-FkNN and CMDW-FkNN, respectively. A comprehensive evaluation study spanning five experimental stages is carried out against six cutting-edge kNN competitors utilizing fifty-four real-world (balanced, imbalanced, noisy, and time series) datasets in order to illustrate the competitiveness of the established models. With CMDW-FkNN comfortably dominating the competition across the vast majority of datasets (specifically UCI, highly-Imbalanced, and Time Series datasets), the results supported by statistical tests, across three assessment metrics-accuracy, F-measure, and ROC-show that both models have significantly more promise than their rivals.

ISSN

0167-739X

Publisher

Elsevier BV

Volume

176

Disciplines

Computer Sciences

Keywords

Data classification, Data mining, FkNN, Fuzzy k-nearest neighbor, kNN, Machine learning

Scopus ID

105017843123

Indexed in Scopus

yes

Open Access

no

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