ArticleBMC bioinformatics2019
FeatureSelect: a software for feature selection based on machine learning approaches.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
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- A novel early stage drip irrigation system cost estimation model based on management and environmental variables.Scientific reports · 2025Article
- An efficient hybrid filter-wrapper method based on improved Harris Hawks optimization for feature selection.BioImpacts : BI · 2025Article
- Determining expression changes of ANO7 and SLC38A4 membrane transporters in colorectal cancer.Heliyon · 2024Article
- Does Maternal HIV Infection Affect Neonatal Outcomes?Current HIV research · 2024Article
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- On the Relationship between Feature Selection Metrics and Accuracy.Entropy (Basel, Switzerland) · 2023Article
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- A voting-based machine learning approach for classifying biological and clinical datasets.BMC bioinformatics · 2023Article
- MetaAnalyst: a user-friendly tool for metagenomic biomarker detection and phenotype classification.BMC medical research methodology · 2022Article
- Developing machine learning-based models to predict intrauterine insemination (IUI) success by address modeling challenges in imbalanced data and providing modification solutions for them.BMC medical informatics and decision making · 2022Article
- Graph-Based Feature Selection Approach for Molecular Activity Prediction.Journal of chemical information and modeling · 2022Article
- Re-wiring and gene expression changes of AC025034.1 and ATP2B1 play complex roles in early-to-late breast cancer progression.BMC genomic data · 2022Article
- A fuzzy logic-based computational method for the repurposing of drugs against COVID-19.BioImpacts : BI · 2022Article
- A Genome-Wide Association Study and Machine-Learning Algorithm Analysis on the Prediction of Facial Phenotypes by Genotypes in Korean Women.Clinical, cosmetic and investigational dermatology · 2022Article
- Gene selection using pyramid gravitational search algorithm.PloS one · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFeature selection, as a preprocessing stage, is a challenging problem in various sciences such as biology, engineering, computer science, and other fields. For this purpose, some studies have introduced tools and softwares such as WEKA. Meanwhile, these tools or softwares are based on filter methods which have lower performance relative to wrapper methods. In this paper, we address this limitation and introduce a software application called FeatureSelect. In addition to filter methods, FeatureSelect consists of optimisation algorithms and three types of learners. It provides a user-friendly and straightforward method of feature selection for use in any kind of research, and can easily be applied to any type of balanced and unbalanced data based on several score functions like accuracy, sensitivity, specificity, etc.
resultsIn addition to our previously introduced optimisation algorithm (WCC), a total of 10 efficient, well-known and recently developed algorithms have been implemented in FeatureSelect. We applied our software to a range of different datasets and evaluated the performance of its algorithms. Acquired results show that the performances of algorithms are varying on different datasets, but WCC, LCA, FOA, and LA are suitable than others in the overall state. The results also show that wrapper methods are better than filter methods.
conclusionsFeatureSelect is a feature or gene selection software application which is based on wrapper methods. Furthermore, it includes some popular filter methods and generates various comparison diagrams and statistical measurements. It is available from GitHub ( https://github.com/LBBSoft/FeatureSelect ) and is free open source software under an MIT license.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.