Multiple-Instance Learning through Optimum-Path Forest
| dc.contributor.author | Afonso, Luis C. S. | |
| dc.contributor.author | Colombo, Danilo | |
| dc.contributor.author | Pereira, Clayton R. [UNESP] | |
| dc.contributor.author | Costa, Kelton A. P. [UNESP] | |
| dc.contributor.author | Papa, Joao P. [UNESP] | |
| dc.contributor.author | IEEE | |
| dc.contributor.institution | Universidade Federal de São Carlos (UFSCar) | |
| dc.contributor.institution | Petr Brasileiro SA | |
| dc.contributor.institution | Universidade Estadual Paulista (Unesp) | |
| dc.date.accessioned | 2020-12-10T19:58:25Z | |
| dc.date.available | 2020-12-10T19:58:25Z | |
| dc.date.issued | 2019-01-01 | |
| dc.description.abstract | Multiple-instance (MI) learning aims at modeling problems that are better described by several instances of a given sample instead of individual descriptions often employed by standard machine learning approaches. In binary-driven MI problems, the entire bag is considered positive if one (at least) sample is labeled as positive. On the other hand, a bag is considered negative if it contains all samples labeled as negative as well. In this paper, we introduced the Optimum-Path Forest (OPF) classifier to the context of multiple-instance learning paradigm, and we evaluated it in different scenarios that range from molecule description, text categorization, and anomaly detection in well-drilling report classification. The experimental results showed that two different OPF classifiers are very much suitable to handle problems in the multiple-instance learning paradigm. | en |
| dc.description.affiliation | UFSCar Fed Univ Selo Carlos, Dept Comp, Sao Carlos, Brazil | |
| dc.description.affiliation | Petr Brasileiro SA, Cenpes, Rio De Janeiro, RJ, Brazil | |
| dc.description.affiliation | UNESP Sao Paulo State Univ, Dept Comp, Bauru, SP, Brazil | |
| dc.description.affiliationUnesp | UNESP Sao Paulo State Univ, Dept Comp, Bauru, SP, Brazil | |
| dc.description.sponsorship | Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) | |
| dc.description.sponsorship | Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) | |
| dc.description.sponsorship | Petrobras | |
| dc.description.sponsorshipId | FAPESP: 2013/07375-0 | |
| dc.description.sponsorshipId | FAPESP: 2014/122361 | |
| dc.description.sponsorshipId | FAPESP: 2016/19403-6 | |
| dc.description.sponsorshipId | FAPESP: 2017/22905-6 | |
| dc.description.sponsorshipId | CNPq: 307066/2017-7 | |
| dc.description.sponsorshipId | CNPq: 427968/2018-6 | |
| dc.description.sponsorshipId | Petrobras: 2014/00545-0 | |
| dc.format.extent | 7 | |
| dc.identifier.citation | 2019 International Joint Conference On Neural Networks (ijcnn). New York: Ieee, 7 p., 2019. | |
| dc.identifier.issn | 2161-4393 | |
| dc.identifier.uri | http://hdl.handle.net/11449/196859 | |
| dc.identifier.wos | WOS:000530893806001 | |
| dc.language.iso | eng | |
| dc.publisher | Ieee | |
| dc.relation.ispartof | 2019 International Joint Conference On Neural Networks (ijcnn) | |
| dc.source | Web of Science | |
| dc.title | Multiple-Instance Learning through Optimum-Path Forest | en |
| dc.type | Trabalho apresentado em evento | |
| dcterms.license | http://www.ieee.org/publications_standards/publications/rights/rights_policies.html | |
| dcterms.rightsHolder | Ieee | |
| unesp.campus | Universidade Estadual Paulista (Unesp), Faculdade de Ciências, Bauru | pt |
| unesp.department | Computação - FC | pt |