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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.)

Learning concept drift with ensembles of optimum-path forest-based classifiers

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Author(s):
Iwashita, Adriana Sayuri [1] ; de Albuquerque, Victor Hugo C. [2] ; Papa, Joao Paulo [3]
Total Authors: 3
Affiliation:
[1] Univ Fed Sao Carlos, Sao Paulo - Brazil
[2] Univ Fortaleza, Fortaleza, Ceara - Brazil
[3] Sao Paulo State Univ, Sao Paulo - Brazil
Total Affiliations: 3
Document type: Journal article
Source: FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE; v. 95, p. 198-211, JUN 2019.
Web of Science Citations: 1
Abstract

Concept drift methods learn patterns in non-stationary environments. Although such behavior is usually not expected in traditional classification problems, in real-world scenarios one can face them very much easier. In such a context, classifiers can be fooled and their effectiveness affected as well. Some examples include theft detection in energy distribution systems, where the consumer's behavior may change suddenly or smoothly, or even churn prediction in mobile companies. In this paper, we introduce the Optimum-Path Forest (OPF) classifier in the context of concept drift, using decisions for concept drift handling based on a committee of OPF classifiers. We consider three distinct perspectives (three rounds of experiments with variations of streaming managements) over publics datasets, being the results compared to the ones obtained by standard OPF. We consider OPF ensemble suitable to work under these dynamic scenarios since its recognition rates were considerably better when compared to traditional OPF. (C) 2019 Elsevier B.V. All rights reserved. (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