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A copula-based classification using agglomerated feature selection_extraction: an application in cervical cancer diagnostic

Year: 2025

Type: article

Abstract: The use of gene-expression datasets has significantly enhanced our understanding of complex diseases such as cancer. The importance of the relationship between genes in analyzing such datasets has been highlighted, indicating their crucial role in diagnosing the disease accurately. In this study, we investigate the associated copulas between attributes to extract fundamental block-related components. Subsequently, we perform a classification algorithm based on these components to classify a labeled target variable. Specifically, examining the practical implications and effectiveness of our approach in real-world scenarios, we provide a novel illustrative application in cervical cancer classification.

Source: Communications Faculty Of Science University of Ankara Series A1Mathematics and Statistics

DOI: 10.31801/cfsuasmas.1517305

Authors: Necla Koçhan, Ayyub Sheikhi

Institutions: İzmir University of Economics, Shahid Bahonar University of Kerman

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

OpenAlex ID: W4414437034

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Cites: 44

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