An efficient binary chimp optimization algorithm for feature selection in biomedical data classification

Pashaei, Elnaz; Pashaei, Elham

doi:10.1007/s00521-021-06775-0

Cited by 75 publications

(41 citation statements)

References 69 publications

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“…In future research, this can also be extended to other real-world situations such as the field of technology (spam mail detection, security threat classification) and the industry-customer purchase behavioral prediction, and more, not just to solve medical problems that currently dominate the research endeavors. The future development of heuristic or metaheuristic approaches may tend toward the use of other classifiers popular, creation of more hybrid local search techniques with metaheuristic methods for more accurate prediction as studied in [20,36,60,136], multi-objective binary techniques creation, application to solving medical diagnosis challenges [180], hybridized wrapper-filter approaches are expected to be developed, and more real-world application areas would be embarked upon [184]. models in learning from a meaningful set of data for prediction and solving real-life problems.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

“…The main shortfall of this method is computational complexity and scalability. Furthermore, Pashaei and Pashaei [ 180 ] presented two binary versions of the ChOA to tackle the feature selection problem. First, S- and V-shaped transfer functions were utilized to convert the continuous search space to binary, and in the second approach, the crossover operator was used to improve its exploratory behavior.…”

Section: Metaheuristic Algorithms For Multiclass Feature Selectionmentioning

confidence: 99%

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Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

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Selecting relevant feature subsets is vital in machine learning, and multiclass feature selection is harder to perform since most classifications are binary. The feature selection problem aims at reducing the feature set dimension while maintaining the performance model accuracy. Datasets can be classified using various methods. Nevertheless, metaheuristic algorithms attract substantial attention to solving different problems in optimization. For this reason, this paper presents a systematic survey of literature for solving multiclass feature selection problems utilizing metaheuristic algorithms that can assist classifiers selects optima or near optima features faster and more accurately. Metaheuristic algorithms have also been presented in four primary behavior-based categories, i.e., evolutionary-based, swarm-intelligence-based, physics-based, and human-based, even though some literature works presented more categorization. Further, lists of metaheuristic algorithms were introduced in the categories mentioned. In finding the solution to issues related to multiclass feature selection, only articles on metaheuristic algorithms used for multiclass feature selection problems from the year 2000 to 2022 were reviewed about their different categories and detailed descriptions. We considered some application areas for some of the metaheuristic algorithms applied for multiclass feature selection with their variations. Popular multiclass classifiers for feature selection were also examined. Moreover, we also presented the challenges of metaheuristic algorithms for feature selection, and we identified gaps for further research studies.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

Section: Metaheuristic Algorithms For Multiclass Feature Selectionmentioning

confidence: 99%

Multiclass feature selection with metaheuristic optimization algorithms: a review

Akinola

Ezugwu

Agushaka

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…(7) An ML model is built using this subset of features. (8) e accuracy of the ML model is calculated, and it is called as the current accuracy. (9) e current accuracy is compared with the previous accuracy as follows:…”

Section: Classificationmentioning

confidence: 99%

“…Hence, these methods are distinguished by their low computational cost and scalability. Examples include information gain (IG), correlation-based feature selection (CFS), Fisher score, ReliefF, chi-squared, mutual information (MI), and minimum redundancy maximum relevance (mRMR) [ 8 ]. In wrapper methods, different feature subsets are evaluated according to the performance of a specific ML model so that the best subset is identified [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis

Elemam

Elshrkawey

2022

The Scientific World Journal

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Cancer is a deadly disease that occurs due to rapid and uncontrolled cell growth. In this article, a machine learning (ML) algorithm is proposed to diagnose different cancer diseases from big data. The algorithm comprises a two-stage hybrid feature selection. In the first stage, an overall ranker is initiated to combine the results of three filter-based feature evaluation methods, namely, chi-squared, F-statistic, and mutual information (MI). The features are then ordered according to this combination. In the second stage, the modified wrapper-based sequential forward selection is utilized to discover the optimal feature subset, using ML models such as support vector machine (SVM), decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) classifiers. To examine the proposed algorithm, many tests have been carried out on four cancerous microarray datasets, employing in the process 10-fold cross-validation and hyperparameter tuning. The performance of the algorithm is evaluated by calculating the diagnostic accuracy. The results indicate that for the leukemia dataset, both SVM and KNN models register the highest accuracy at 100% using only 5 features. For the ovarian cancer dataset, the SVM model achieves the highest accuracy at 100% using only 6 features. For the small round blue cell tumor (SRBCT) dataset, the SVM model also achieves the highest accuracy at 100% using only 8 features. For the lung cancer dataset, the SVM model also achieves the highest accuracy at 99.57% using 19 features. By comparing with other algorithms, the results obtained from the proposed algorithm are superior in terms of the number of selected features and diagnostic accuracy.

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“…The hybrid feature selection algorithm combines the advantages of the above three algorithms and is the mainstream algorithm for feature selection tasks [ 16 – 18 ]. For example, researchers can combine the filtering method and packaging method to realize the rapid filtering of invalid features in the filtering method and reduce the time complexity of the packaging method to design an efficient packaging method for further selection and optimization of features.…”

Section: Introductionmentioning

confidence: 99%

A novel biomarker selection method combining graph neural network and gene relationships applied to microarray data

Xie

Zhang

et al. 2022

BMC Bioinformatics

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Background The discovery of critical biomarkers is significant for clinical diagnosis, drug research and development. Researchers usually obtain biomarkers from microarray data, which comes from the dimensional curse. Feature selection in machine learning is usually used to solve this problem. However, most methods do not fully consider feature dependence, especially the real pathway relationship of genes. Results Experimental results show that the proposed method is superior to classical algorithms and advanced methods in feature number and accuracy, and the selected features have more significance. Method This paper proposes a feature selection method based on a graph neural network. The proposed method uses the actual dependencies between features and the Pearson correlation coefficient to construct graph-structured data. The information dissemination and aggregation operations based on graph neural network are applied to fuse node information on graph structured data. The redundant features are clustered by the spectral clustering method. Then, the feature ranking aggregation model using eight feature evaluation methods acts on each clustering sub-cluster for different feature selection. Conclusion The proposed method can effectively remove redundant features. The algorithm’s output has high stability and classification accuracy, which can potentially select potential biomarkers.

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An efficient binary chimp optimization algorithm for feature selection in biomedical data classification

Cited by 75 publications

References 69 publications

Multiclass feature selection with metaheuristic optimization algorithms: a review

Multiclass feature selection with metaheuristic optimization algorithms: a review

A Highly Discriminative Hybrid Feature Selection Algorithm for Cancer Diagnosis

A novel biomarker selection method combining graph neural network and gene relationships applied to microarray data

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