Document details

Description

Made available in DSpace on 2020-12-10T19:58:25Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-01-01

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Petrobras

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.

UFSCar Fed Univ Selo Carlos, Dept Comp, Sao Carlos, Brazil

Petr Brasileiro SA, Cenpes, Rio De Janeiro, RJ, Brazil

UNESP Sao Paulo State Univ, Dept Comp, Bauru, SP, Brazil

UNESP Sao Paulo State Univ, Dept Comp, Bauru, SP, Brazil

FAPESP: 2013/07375-0

FAPESP: 2014/122361

FAPESP: 2016/19403-6

FAPESP: 2017/22905-6

CNPq: 307066/2017-7

CNPq: 427968/2018-6

Petrobras: 2014/00545-0