ArticleScientific reports2017
OmniGA: Optimized Omnivariate Decision Trees for Generalizable Classification Models.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity.Journal of hazardous materials · 2024Article
- Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA.Gene: X · 2020Article
- In vivo non-invasive staining-free visualization of dermal mast cells in healthy, allergy and mastocytosis humans using two-photon fluorescence lifetime imaging.Scientific reports · 2020Article
- DeepGSR: an optimized deep-learning structure for the recognition of genomic signals and regions.Bioinformatics (Oxford, England) · 2019Article
- DPubChem: a web tool for QSAR modeling and high-throughput virtual screening.Scientific reports · 2018Article
- Omni-PolyA: a method and tool for accurate recognition of Poly(A) signals in human genomic DNA.BMC genomics · 2017Article
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Authors and funding
2 authors.
Funding
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Abstract
Classification problems from different domains vary in complexity, size, and imbalance of the number of samples from different classes. Although several classification models have been proposed, selecting the right model and parameters for a given classification task to achieve good performance is not trivial. Therefore, there is a constant interest in developing novel robust and efficient models suitable for a great variety of data. Here, we propose OmniGA, a framework for the optimization of omnivariate decision trees based on a parallel genetic algorithm, coupled with deep learning structure and ensemble learning methods. The performance of the OmniGA framework is evaluated on 12 different datasets taken mainly from biomedical problems and compared with the results obtained by several robust and commonly used machine-learning models with optimized parameters. The results show that OmniGA systematically outperformed these models for all the considered datasets, reducing the F
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Registered trials
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