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A New Evolutionary Ensemble Learning of Multimodal Feature Selection from Microarray Data

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A Correction to this article was published on 21 June 2024

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Abstract

In the last decades, data has grown exponentially with respect to the number of samples and features. This makes the feature selection (FS) more challenging. In this paper, an optimization method called the multimodal optimization (MMO) technique is employed to find multiple optimal solutions instead of a single solution. The main contribution of the MMO technique is to provide multiple optimal solutions, instead of a single solution. Using the hidden information in the data and creating an ensemble of classifiers, the potential and information of multiple answers provided by MMO are used to address the issue of FS from microarray data. After pre-processing of the data, to benefit from the potential and information of multiple answers, the optimal features subset are obtained by a firefly-based MMO algorithm. The mutual information method is used as the fitness function to evaluate the proposed subset of features. Then, each feature subset is used to train a classifier and the classifiers are trained by the data, the features of which are presented by a MMO algorithm, and these classifiers make an ensemble. To select a proper combination, a particle swarm optimization algorithm is used. Finally, the algorithm for the datasets of the microarray is evaluated in terms of cancer diagnosis. The proposed method efficiency is evaluated by applying on 11 datasets. The results indicate the superiority and proper performance of the multimodal FS method compared to other methods.

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Notes

  1. https://csse.szu.edu.cn/staff/zhuzx/Datasets.html.

  2. https://github.com/kivancguckiran/microarray-data.

  3. https://file.biolab.si/biolab/supp/bi-cancer/projections/index.html.

  4. https://leo.ugr.es/elvira/DBCRepository/ProstateCancer/ProstateCancer.html.

  5. https://leo.ugr.es/elvira/DBCRepository/DLBCL/DLBCL-Stanford.html.

  6. https://github.com/nadianekouie/multimodal-feature-selection.

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  1. Department of Computer Engineering, Kashan Branch, Islamic Azad University, Kashan, Iran

    Nadia Nekouie, Morteza Romoozi & Mahdi Esmaeili

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  1. Nadia Nekouie
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  2. Morteza Romoozi
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Nekouie, N., Romoozi, M. & Esmaeili, M. A New Evolutionary Ensemble Learning of Multimodal Feature Selection from Microarray Data. Neural Process Lett 55, 6753–6780 (2023). https://doi.org/10.1007/s11063-023-11159-7

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