Algoritmo evolutivo com representação inteira para seleção de características
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Data
2017-04-20
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Universidade Federal de Goiás
Resumo
Machine learning problems usually involve a large number of features or variables. In
this context, feature selection algorithms have the challenge of determining a reduced
subset from the original set. The main difficulty in this task is the high number of solutions
available in the search space. In this context, genetic algorithm is one of the
most used techniques in this type of problem due to its implicit parallelism in the exploration
of the search space of the problem considered. However, a binary type representation
is usually used to encode the solutions. This work proposes an implementation
solution that makes use of integer representation called intEA-MLR instead of binary.
The integer representation optimizes the understanding of the data, as the features
to be selected are represented by integer values, reducing the size of the chromosome
used in the search process. The intEA-MLR in this context is presented as an alternative
way of solving high dimensional problems in regression problems. As a case study,
three different sets of data are used concerning problems involving determination of properties
of interest in samples of 1) Grain Wheat, 2) Medicine tablets and 3) petroleum.
Such sets were used in competitions held at the International Diffuse Reflectance Conference
(IDRC) (http://cnirs.clubexpress.com/content.aspx?page_id=22&club_
id=409746&module_id=190211), in the years 2008, 2012 and 2014, respectively. The results
showed that the proposed solution was able to improve the obtained solutions when
compared to the classical implementation that makes use of binary coding, with both more
accurate prediction models and with reduced number of features. IntEA-MLR also outperformed
the competition winners, reaching 91.17% better than the competition winner
for the petroleum data set. In addition, the results also indicated that the computation time
required by the intEA-MLR is relatively smaller as more features are available.
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Citação
SOUSA, R. S. Algoritmo evolutivo com representação inteira para seleção de características. 2017. 64 f.
Dissertação (Mestrado Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2017.