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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Improving optimum-path forest learning using bag-of-classifiers and confidence measures

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Author(s):
Nachif Fernandes, Silas Evandro [1] ; Papa, Joao Paulo [2]
Total Authors: 2
Affiliation:
[1] Fed Univ Sao Carlos UFSCar, Dept Comp, Rodovia Washington Luis, Km 235 SP 310, BR-13565905 Sao Carlos, SP - Brazil
[2] Sao Paulo State Univ UNESP, Dept Comp, Av Engn Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru, SP - Brazil
Total Affiliations: 2
Document type: Journal article
Source: PATTERN ANALYSIS AND APPLICATIONS; v. 22, n. 2, p. 703-716, MAY 2019.
Web of Science Citations: 1
Abstract

Machine learning techniques have been actively pursued in the last years, mainly due to the great number of applications that make use of some sort of intelligent mechanism for decision-making processes. In this work, we presented an ensemble of optimum-path forest (OPF) classifiers, which consists into combining different instances that compute a score-based confidence level for each training sample in order to turn the classification process smarter, i.e., more reliable. Such confidence level encodes the level of effectiveness of each training sample, and it can be used to avoid ties during the OPF competition process. Experimental results over fifteen benchmarking datasets have shown the effectiveness and efficiency of the proposed approach for classification problems, with more accurate results in more than 67% of the datasets considered in this work. Additionally, we also considered a bagging strategy for comparison purposes, and we showed the proposed approach can lead to considerably better results. (AU)

FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants
FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
Grantee:Alexandre Xavier Falcão
Support Opportunities: Research Projects - Thematic Grants
FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants