Gene expression selection for cancer classification using intelligent collaborative filtering and hamming distance guided multi-objective swarm optimization

High dimensional microarray cancer datasets contain thousands of genes with a very few numbers of samples. High class imbalance, presence of noisy and redundant genes and overlapping nature of extracted features among different disease classes deteriorate the disease prediction accuracy. An intellig...

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Vydáno v:Applied soft computing Ročník 170; s. 112654
Hlavní autoři: Agarwalla, Prativa, Mukhopadhyay, Sumitra
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.02.2025
Témata:
Classification
DNA microarray
Feature selection
Multi-objective algorithm
Particle swarm optimization (PSO)
DNA microarray
Feature selection
Multi-objective algorithm
Particle swarm optimization (PSO)
Classification
ISSN:1568-4946
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Shrnutí:High dimensional microarray cancer datasets contain thousands of genes with a very few numbers of samples. High class imbalance, presence of noisy and redundant genes and overlapping nature of extracted features among different disease classes deteriorate the disease prediction accuracy. An intelligent collaborative filtering (ICF) assisted and hamming distance guided multi-objective swarm intelligence framework (HIMS) is proposed for efficient selection of optimal gene set for disease identification. In the framework, first intelligent collaborative filtering (ICF) has been introduced to improve the prediction ability which combines the features from different feature selection tools. Then, a multi-objective multi-population search (MOMPS) algorithm has been proposed which contributes as a core part of HIMS. It generates more diversified solutions by avoiding local trapping. Hamming distance operator has been applied here as an alternative of sorting mechanism for the selection of Pareto optimal solutions. It also helps to reduce the computational complexity. Along with that, a time-varying U-shaped function is introduced for the binary conversion process for feature selection. Extensive experiments were conducted on 16 different single and multi-class datasets to study the efficacy of HIMS. The experimental results show that HIMS performs favorably well in comparison with other existing techniques with fewer numbers of genes. •Intelligent collaborative filtering from multimodal filter source.•Interactive multi-objective multi-population search (MOMPS) for gene selection.•Hamming distance operator based dominant gene selection.•Time-variant U-shaped discretization function for gene selection.•Adaptively tuned classifier for cancer classification with optimally selected genes.
ISSN:1568-4946
DOI:10.1016/j.asoc.2024.112654