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    Area of Science:

    • Computational intelligence
    • Data science
    • Bioinformatics
    • Signal processing

    Background:

    • Feature selection is crucial for classification tasks in fields like bioinformatics and signal processing.
    • Traditional methods often overlook the trade-off between classification accuracy and feature costs.
    • Cost-based feature selection addresses scenarios requiring optimization of both performance and cost.

    Purpose of the Study:

    • To present the first application of multi-objective particle swarm optimization (PSO) for cost-based feature selection.
    • To generate a Pareto front of non-dominated feature subsets, catering to diverse decision-maker needs.
    • To develop a competitive algorithm for optimizing classification performance while minimizing feature costs.

    Main Methods:

    • Utilized a multi-objective particle swarm optimization (PSO) framework.
    • Incorporated probability-based encoding, a hybrid operator, crowding distance, external archive, and Pareto domination.
    • Compared the proposed PSO-based algorithm against existing multi-objective feature selection methods on benchmark datasets.

    Main Results:

    • The proposed algorithm successfully evolved a set of non-dominated solutions (feature subsets).
    • Demonstrated the ability to automatically balance classification performance and feature costs.
    • Experimental results confirmed the algorithm's high competitiveness in cost-based feature selection.

    Conclusions:

    • The developed multi-objective PSO is effective for cost-based feature selection.
    • It provides decision-makers with a range of optimal trade-offs between classification accuracy and feature costs.
    • This approach offers a significant advancement for practical applications requiring efficient and cost-aware feature selection.