Differential Evolution for Classification: A Novel Classifier Technique in Data Mining

Simple item page
dc.contributor.authorFarda, Irfan
dc.contributor.authorThammano, Arit
dc.date.accessioned2026-08-06T10:44:42Z
dc.date.available2026-08-06T10:44:42Z
dc.date.issued2024-01-01
dc.description.abstractIn the realm of Data Mining, the pursuit of innovative classification methodologies remains crucial for advancing robust techniques in handling complex and diverse datasets. This paper explores the application of Differential Evolution (DE), a powerful optimization algorithm, as a unique and effective optimization-based standalone classifier. Our exploration focused on harnessing DE's intrinsic capabilities, adapting it into a classifier while preserving its distinctiveness. The fundamental principle of DE for classification involves iteratively optimizing the center point for each class using DE operators, which are specialized mechanisms for exploring and refining solutions, and utilizing these optimized points for making predictions. The effectiveness of our model was evaluated on ten classification datasets from the UCI Machine Learning Repository and compared against three other classification methods: KNN, ZMP, and BPNN. Experimental results underscore the competitive performance of our proposed model, emphasizing the potential of DE in effectively addressing classification challenges.
dc.identifier.citationKst 2024 16th International Conference on Knowledge and Smart Technology, 220-225, 2024
dc.identifier.doi10.1109/KST61284.2024.10499680
dc.identifier.other2-s2.0-85191660963
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/15270
dc.sourceKst 2024 16th International Conference on Knowledge and Smart Technology
dc.subjectclassification
dc.subjectdata mining
dc.subjectdifferential evolution
dc.titleDifferential Evolution for Classification: A Novel Classifier Technique in Data Mining
dc.typeConference Paper

Files

Collections

Research