• Feature Selection Using Geomet...

Treffer: Feature Selection Using Geometric Semantic Genetic Programming

Title:
Feature Selection Using Geometric Semantic Genetic Programming
Authors:
Rosa, G. H., Papa, J. P., Papa, L. P., Ochoa, G.
Contributors:
Universidade Estadual Paulista (UNESP)
Publisher Information:
Assoc Computing Machinery
Publication Year:
2017
Collection:
Universidade Estadual Paulista São Paulo: Repositório Institucional UNESP
Subject Terms:
Feature selection, Geometric Semantic Genetic Programming
Document Type:
Konferenz conference object
File Description:
253-254
Language:
English
Relation:
Proceedings Of The 2017 Genetic And Evolutionary Computation Conference Companion (gecco'17 Companion); http://dx.doi.org/10.1145/3067695.3076020; Proceedings Of The 2017 Genetic And Evolutionary Computation Conference Companion (gecco'17 Companion). New York: Assoc Computing Machinery, p. 253-254, 2017.; http://hdl.handle.net/11449/210101; WOS:000625865500127
DOI:
10.1145/3067695.3076020
Availability:
http://hdl.handle.net/11449/210101
https://doi.org/10.1145/3067695.3076020
Accession Number:
edsbas.42211EA
Database:
BASE

Weitere Informationen

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) ; Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ; Processo FAPESP: 2014/162509 ; Processo FAPESP: 2014/12236-1 ; Processo FAPESP: 2015/25739-4 ; CNPq: 306166/2014-3 ; 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. 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.