Enhancing discrimination power with genetic feature construction: a grammatical evolution approach

Enhancing discrimination power with genetic feature construction: a grammatical evolution approach

Author Miquilini, Patricia Autor UNIFESP Google Scholar
Barros, Rodrigo C. Google Scholar
de Melo, Vinicius V. Autor UNIFESP Google Scholar
Basgalupp, Marcio P. Autor UNIFESP Google Scholar
Abstract Data set preprocessing is a critical step for the successful application of machine learning algorithms in classification tasks. Even though we rely on learning algorithms to pinpoint the optimal decision boundaries in the feature space by properly detecting latent relationships among the input features, their performance is often bounded by the discriminative power of the available features. Therefore, much effort has been devoted to developing preprocessing methods that are capable of transforming the input data with the final goal of aiding the machine learning algorithm in building high-quality classification models. One such a method is feature construction, which is a flexible preprocessing procedure that exploits linear and nonlinear transformations of the original feature space in an attempt to capture useful information that is not explicit in the original data. Since the task of feature construction can be modelled as a heuristic search in the space of novel latent features, this paper investigates an evolutionary approach for performing such a task, namely grammatical evolution (GE). In our proposed approach, GE is employed for building an extra novel feature from the available input data in order to maximize the predictive performance of the learning algorithm in training data. Results show that many interesting implicit relationships are indeed found by the evolutionary approach, improving the performance of two well-known decision-tree induction algorithms.
Keywords Classification
Language English
Sponsor Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Date 2016
Published in 2016 Ieee Congress On Evolutionary Computation (CEC). New york, p. 3824-3831, 2016.
Publisher Acta Dermato-Venereologica
Extent 3824-3831
Origin http://dx.doi.org/10.1109/CEC.2016.7744274
Access rights Closed access
Type Conference paper
Web of Science ID WOS:000390749104002
URI http://repositorio.unifesp.br/handle/11600/49306

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