ArticleScientific reports2019
An Efficient hybrid filter-wrapper metaheuristic-based gene selection method for high dimensional datasets.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
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- Identifying candidate biomarkers for detecting bronchogenic carcinoma stages using metaheuristic algorithms based on information fusion theory.Discover oncology · 2025Article
- An efficient hybrid filter-wrapper method based on improved Harris Hawks optimization for feature selection.BioImpacts : BI · 2025Article
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- Hybrid rice optimization algorithm inspired grey wolf optimizer for high-dimensional feature selection.Scientific reports · 2024Article
- A novel feature selection algorithm for identifying hub genes in lung cancer.Scientific reports · 2023Article
- Hybrid black widow optimization with iterated greedy algorithm for gene selection problems.Heliyon · 2023Article
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- A new hybrid algorithm for three-stage gene selection based on whale optimization.Scientific reports · 2023Article
- Drug-target interaction prediction based on protein features, using wrapper feature selection.Scientific reports · 2023Article
- A novel hybrid algorithm based on Harris Hawks for tumor feature gene selection.PeerJ. Computer science · 2023Article
- Effective hybrid feature selection using different bootstrap enhances cancers classification performance.BioData mining · 2022Article
- A Machine Learning Method with Filter-Based Feature Selection for Improved Prediction of Chronic Kidney Disease.Bioengineering (Basel, Switzerland) · 2022Article
- Hybrid learning method based on feature clustering and scoring for enhanced COVID-19 breath analysis by an electronic nose.Artificial intelligence in medicine · 2022Article
- A hybrid feature extraction scheme for efficient malonylation site prediction.Scientific reports · 2022Article
- A Modified Memetic Algorithm with an Application to Gene Selection in a Sheep Body Weight Study.Animals : an open access journal from MDPI · 2022Article
- Multiclass feature selection with metaheuristic optimization algorithms: a review.Neural computing & applications · 2022Review
- Cluster Analysis of Cell Nuclei in H&E-Stained Histological Sections of Prostate Cancer and Classification Based on Traditional and Modern Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2021Article
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4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Feature selection problem is one of the most significant issues in data classification. The purpose of feature selection is selection of the least number of features in order to increase accuracy and decrease the cost of data classification. In recent years, due to appearance of high-dimensional datasets with low number of samples, classification models have encountered over-fitting problem. Therefore, the need for feature selection methods that are used to remove the extensions and irrelevant features is felt. Recently, although, various methods have been proposed for selecting the optimal subset of features with high precision, these methods have encountered some problems such as instability, high convergence time, selection of a semi-optimal solution as the final result. In other words, they have not been able to fully extract the effective features. In this paper, a hybrid method based on the IWSSr method and Shuffled Frog Leaping Algorithm (SFLA) is proposed to select effective features in a large-scale gene dataset. The proposed algorithm is implemented in two phases: filtering and wrapping. In the filter phase, the Relief method is used for weighting features. Then, in the wrapping phase, by using the SFLA and the IWSSr algorithms, the search for effective features in a feature-rich area is performed. The proposed method is evaluated by using some standard gene expression datasets. The experimental results approve that the proposed approach in comparison to similar methods, has been achieved a more compact set of features along with high accuracy. The source code and testing datasets are available at https://github.com/jimy2020/SFLA_IWSSr-Feature-Selection.
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