Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Meta-heuristic multi- and many-objective optimization techniques for solution of machine learning problems

Full text
Author(s):
Rodrigues, Douglas [1] ; Papa, Joao P. [2] ; Adeli, Hojjat [3, 4, 5]
Total Authors: 3
Affiliation:
[1] Univ Fed Sao Carlos, Dept Comp, Sao Carlos, SP - Brazil
[2] Sao Paulo State Univ, Dept Comp, Bauru, SP - Brazil
[3] Ohio State Univ, Dept Biomed Engn, Columbus, OH 43210 - USA
[4] Ohio State Univ, Dept Civil Environm & Geodet Engn, Columbus, OH 43210 - USA
[5] Ohio State Univ, Dept Neurosci, Columbus, OH 43210 - USA
Total Affiliations: 5
Document type: Review article
Source: EXPERT SYSTEMS; v. 34, n. 6 DEC 2017.
Web of Science Citations: 2
Abstract

Recently, multi- and many-objective meta-heuristic algorithms have received considerable attention due to their capability to solve optimization problems that require more than one fitness function. This paper presents a comprehensive study of these techniques applied in the context of machine learning problems. Three different topics are reviewed in this work: (a) feature extraction and selection, (b) hyper-parameter optimization and model selection in the context of supervised learning, and (c) clustering or unsupervised learning. The survey also highlights future research towards related areas. (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: 15/50319-9 - Meta-heuristic-based optimization of probabilistic neural networks
Grantee:João Paulo Papa
Support Opportunities: Regular Research Grants
FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
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