An Efficient SVM-Based Feature Selection Model for Cancer Classification Using High-Dimensional Microarray Data

Feature selection is critical in analyzing microarray data, which has many features (genes) or dimensions. However, with only a few samples the large search space and time consumed during their selection make selecting relevant and informative genes that improve classification performance a complex...

Description complète

Enregistré dans:
Détails bibliographiques
Auteurs principaux: Passent El Kafrawy, Hanaa Fathi, Mohammed Qaraad, Ayda K. Kelany, Xumin Chen
Format: article
Langue:EN
Publié: IEEE 2021
Sujets:
Accès en ligne:https://doaj.org/article/fc9ce6eb64744b63be5363e9993b5579
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
Description
Résumé:Feature selection is critical in analyzing microarray data, which has many features (genes) or dimensions. However, with only a few samples the large search space and time consumed during their selection make selecting relevant and informative genes that improve classification performance a complex task. This paper proposed a hybrid model for gene selection known as (SVM-mRMRe), the proposed model provides a framework for combining filter-based, ensemble, and embedded methods to select the most relevant and informative genes from high-dimensional microarray data by fusing embedded SVM coefficients (features ranking) with ensemble mRMRe. Eight of the most commonly used microarray datasets for various types of cancer were used to evaluate the model. The selected subset feature is evaluated by four different types of classifiers: random forest (RF), multilayer perceptron (MLP), k-nearest neighbors (k-NN), and Support Vector Machine (SVM). The experimental results show that the proposed model reduces time consumption and dimensionality and improves the differentiation of cancer tissues from benign tissues. Furthermore, the selected genes for the brain cancer dataset are biologically interpreted, and it agrees with the findings of relevant biomedical studies and plays an important role in patient prognosis.