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pro vyhledávání: '"Victor H. Barella"'
Autor:
André C. P. L. F. de Carvalho, Ana Carolina Lorena, Marcilio C. P. de Souto, Luís P. F. Garcia, Victor H. Barella
Publikováno v:
Information Sciences
Information Sciences, Elsevier, 2021, 553, pp.83-109. ⟨10.1016/j.ins.2020.12.006⟩
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Information Sciences, Elsevier, 2021, 553, pp.83-109. ⟨10.1016/j.ins.2020.12.006⟩
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Imbalanced datasets are an important challenge in supervised Machine Learning (ML). According to the literature, class imbalance does not necessarily impose difficulties for ML algorithms. Difficulties mainly arise from other characteristics, such as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4142f54211096b5a9c2eadd4c1f6b582
https://doi.org/10.1016/j.ins.2020.12.006
https://doi.org/10.1016/j.ins.2020.12.006
Publikováno v:
Intelligent Systems ISBN: 9783030613792
BRACIS (2)
BRACIS (2)
Classification tasks using imbalanced datasets are not challenging on their own. Classification models perform poorly on the minority class when the datasets present other difficulties, such as class overlap and complex decision border. Data complexi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::63dec7a65f1f75997120b4eae4bb2e05
https://doi.org/10.1007/978-3-030-61380-8_34
https://doi.org/10.1007/978-3-030-61380-8_34
Publikováno v:
BRACIS
Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalanced data poses
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d9978c0597ffe7e30e645e8803ebe35f
https://doi.org/10.1109/bracis.2019.00045
https://doi.org/10.1109/bracis.2019.00045
Autor:
André C. P. L. F. de Carvalho, Luís P. F. Garcia, Ana Carolina Lorena, Marcilio Carlos Pereira de Souto, Victor H. Barella
Publikováno v:
IJCNN
IEEE International Joint Conference on Neural Networks
IEEE International Joint Conference on Neural Networks, Jul 2018, Rio de Janeiro, Brazil. pp.1-8, ⟨10.1109/IJCNN.2018.8489661⟩
IEEE International Joint Conference on Neural Networks
IEEE International Joint Conference on Neural Networks, Jul 2018, Rio de Janeiro, Brazil. pp.1-8, ⟨10.1109/IJCNN.2018.8489661⟩
In imbalanced classification tasks, the training datasets may show class overlapping and classes of low density. In these scenarios, the predictions for the minority class are impaired. Although assessing the imbalance level of a training set is stra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c7c84a3c3844b423808d05f635d41cc6
https://doi.org/10.1109/ijcnn.2018.8489661
https://doi.org/10.1109/ijcnn.2018.8489661
Publikováno v:
Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
Made available in DSpace on 2018-12-11T17:20:08Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-05-01 Profile hidden Markov models (pHMMs) have been used to search for transposable elements (TEs) in genomes. For the learning of pHMMs aimed to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da0d70b239f8a81b1c95d7135795df25