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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.)

A binary-constrained Geometric Semantic Genetic Programming for feature selection purposes

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Papa, Joao Paulo [1] ; Rosa, Gustavo Henrique [1] ; Papa, Luciene Patrici [2]
Total Authors: 3
[1] Sao Paulo State Univ, Dept Comp, Av Eng Luiz Edmundo Carrijo Coube 14-01, BR-17033360 Bauru - Brazil
[2] Sao Paulo Southwestern Coll, Av Prof Celso Ferreira Silva 1001, 14-01, BR-18707150 Avare - Brazil
Total Affiliations: 2
Document type: Journal article
Source: PATTERN RECOGNITION LETTERS; v. 100, p. 59-66, DEC 1 2017.
Web of Science Citations: 3

Feature selection concerns the task of finding the subset of features that are most relevant to some specific problem in the context of machine learning. By selecting proper features, one can reduce the computational complexity of the learned model, and to possibly enhance its effectiveness by reducing the well-known overfitting. During the last years, the problem of feature selection has been modeled as an optimization task, where the idea is to find the subset of features that maximize some fitness function, which can be a given classifier's accuracy or even some measure concerning the samples' separability in the feature space, for instance. In this paper, we introduced Geometric Semantic Genetic Programming (GSGP) in the context of feature selection, and we experimentally showed it can work properly with both conic and non-conic fitness landscapes. We observed that there is no need to restrict the feature selection modeling into GSGP constraints, which can be quite useful to adopt the semantic operators to a broader range of applications. (C) 2017 Elsevier B.V. All rights reserved. (AU)

FAPESP's process: 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms
Grantee:João Paulo Papa
Support type: Regular Research Grants
FAPESP's process: 15/25739-4 - On the Study of Semantics in Deep Learning Models
Grantee:Gustavo Henrique de Rosa
Support type: Scholarships in Brazil - Master
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:José Alberto Cuminato
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 16/19403-6 - Energy-based learning models and their applications
Grantee:João Paulo Papa
Support type: 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 type: Research Projects - Thematic Grants
FAPESP's process: 10/15566-1 - Evaluation of mathematic modell for study of fine motor function in individuals with Parkinson's disease using a multi-sensor biometric smart pen, BiSP
Grantee:Silke Anna Theresa Weber
Support type: Regular Research Grants