Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola, Olatunji; Ezugwu, Absalom E.; Agushaka, Jeffrey O.; Zitar, Raed Abu; Abualigah, Laith

doi:10.1007/s00521-022-07705-4

Cited by 82 publications

(39 citation statements)

References 285 publications

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“…All tasks were binary classification problems, preceded by feature selection. While there are several feature selection methods that can be used including metaheuristic algorithms [ 44 ], since the primary goal of the study is to showcase the utility of SHAP, a simplified machine learning workflow was used ( Fig 2 ).…”

Section: Resultsmentioning

confidence: 99%

Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification

Bifarin

2023

PLoS ONE

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Machine learning (ML) models are used in clinical metabolomics studies most notably for biomarker discoveries, to identify metabolites that discriminate between a case and control group. To improve understanding of the underlying biomedical problem and to bolster confidence in these discoveries, model interpretability is germane. In metabolomics, partial least square discriminant analysis (PLS-DA) and its variants are widely used, partly due to the model’s interpretability with the Variable Influence in Projection (VIP) scores, a global interpretable method. Herein, Tree-based Shapley Additive explanations (SHAP), an interpretable ML method grounded in game theory, was used to explain ML models with local explanation properties. In this study, ML experiments (binary classification) were conducted for three published metabolomics datasets using PLS-DA, random forests, gradient boosting, and extreme gradient boosting (XGBoost). Using one of the datasets, PLS-DA model was explained using VIP scores, while one of the best-performing models, a random forest model, was interpreted using Tree SHAP. The results show that SHAP has a more explanation depth than PLS-DA’s VIP, making it a powerful method for rationalizing machine learning predictions from metabolomics studies.

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Section: Resultsmentioning

confidence: 99%

Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification

Bifarin

2023

PLoS ONE

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“…Many proposed metaheuristic algorithms have provided optimal or near-optimal solutions to many real-world applications, including various feature selection problems [ 24 ]. Some of which include the Whale Optimization Algorithm (WOA) and its hybrid [ 16 , 25 , 26 ], Cuckoo Search Optimization Algorithm (CSO) [ 27 – 29 ], DragonFly Algorithm (DA) (Chantar et al, 2021; Cui et al, 2020; Sree Ranjini & Murugan, 2017) [ 30 – 32 ], Prairie Dog Optimization (PDO) Algorithm [ 33 ] and many more.…”

Section: Related Literaturementioning

confidence: 99%

Binary dwarf mongoose optimizer for solving high-dimensional feature selection problems

2022

Self Cite

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Selecting appropriate feature subsets is a vital task in machine learning. Its main goal is to remove noisy, irrelevant, and redundant feature subsets that could negatively impact the learning model’s accuracy and improve classification performance without information loss. Therefore, more advanced optimization methods have been employed to locate the optimal subset of features. This paper presents a binary version of the dwarf mongoose optimization called the BDMO algorithm to solve the high-dimensional feature selection problem. The effectiveness of this approach was validated using 18 high-dimensional datasets from the Arizona State University feature selection repository and compared the efficacy of the BDMO with other well-known feature selection techniques in the literature. The results show that the BDMO outperforms other methods producing the least average fitness value in 14 out of 18 datasets which means that it achieved 77.77% on the overall best fitness values. The result also shows BDMO demonstrating stability by returning the least standard deviation (SD) value in 13 of 18 datasets (72.22%). Furthermore, the study achieved higher validation accuracy in 15 of the 18 datasets (83.33%) over other methods. The proposed approach also yielded the highest validation accuracy attainable in the COIL20 and Leukemia datasets which vividly portray the superiority of the BDMO.

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“…Quantum feature selection Feature selection is also a dimension reduction algorithm [34]. Its core is to select the feature subset that best represents the overall data information through loss function and sparse regular term.…”

Section: Quantum Dimensionality Reductionmentioning

confidence: 99%

Robust Quantum Feature Selection Algorithm

Zhang¹,

Li²,

Xu³

et al. 2022

Preprint

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<p>High dimensional data has been a notoriously challenging issue. Existing quantum dimension reduction technology mainly focuses on quantum principal component analysis. There are only a few works on the direction of quantum feature selection algorithm which they are not robust. Also, there are few quantum circuits designed for feature selection, in which some steps are not quantized yet. For example, existing quantum circuits cannot solve the objective function based on sparse learning. To deal with these issues, this paper proposes a robust quantum feature selection by designing a new quantum circuit. Specifically, the sparse regularization term and least squares loss are first applied to construct the proposed objective function. And then, six kinds of quantum registers and their initial states are prepared. In addition, quantum techniques, such as quantum phase estimation and controlled rotation, are employed to construct an alternating iterative quantum circuit to obtain the final quantum state of the feature selection variable. Finally, a series of experiments are conducted to verify that the proposed algorithm can accurately select important features and has good robustness. <br> </p>

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Multiclass feature selection with metaheuristic optimization algorithms: a review

Cited by 82 publications

References 285 publications

Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification

Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification

Binary dwarf mongoose optimizer for solving high-dimensional feature selection problems

Robust Quantum Feature Selection Algorithm

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