Development and evaluation of a novel framework to enhance k-NN algorithm's accuracy in data sparsity contexts.

ABSTRACT: This paper presents a novel framework for implementing the k-NN algorithm, designed to enhance its accuracy in contexts with sparse data. The framework addresses limitations in the algorithm's training process by optimizing data structures. It employs composite datasets generated from the initial data using a data-driven fuzzy Analytic Hierarchy Process weighting scheme. This approach is designed to enhance the informational content in the initial datasets, thus reducing the entropy and implementation uncertainty. The framework was evaluated using 75 publicly available datasets and 3 generated datasets, demonstrating significant accuracy improvements across various k-parameter values. The findings were rigorously generalized using non-parametric hypothesis tests; while the resulting sensit

SUBMITTER: Giannopoulos PG 

PROVIDER: S-EPMC11499816 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature