Optimising Genes Selection with Greedy Heuristic Fuzzy Clustering for Binary Classification Problems

doi:10.1016/j.asoc.2025.114092

Optimising Genes Selection with Greedy Heuristic Fuzzy Clustering for Binary Classification Problems

2025-10-18

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DOI：10.1016/j.asoc.2025.114092

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• The study introduces a novel method, GHFClust, designed to enhance accuracy and address high dimensionality issues in datasets. • With the increasing use of microarray data, reducing data dimensions has become a significant focus in research, necessitating the use of feature selection and clustering techniques. • A Reliability analysis and other performance metrics and statistics is used to assess the method with existing methods. • The GHFClust method demonstrates a higher accuracy rate when applied to benchmark datasets, utilizing the fuzzy-c-means clustering technique for the remaining data.

Keywords:

Computational modelling

Gene expression

Fuzzy clustering

Filter algorithms

Feature extraction

Machine learning

Optimising Genes Selection with Greedy Heuristic Fuzzy Clustering for Binary Classification Problems

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