A Research of Variable Selection Method within A Framework of Real-coded Genetic Algorithm
| Autor: | Setsuya Kurahashi, Takahiro Obata |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Flexibility (engineering)
Mathematical optimization Computer science media_common.quotation_subject Feature selection 02 engineering and technology Set (abstract data type) Bayesian information criterion Genetic algorithm 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Akaike information criterion Selection (genetic algorithm) media_common |
| Zdroj: | CEC |
| DOI: | 10.1109/cec.2019.8790346 |
| Popis: | Recently variable selection and parameter optimization are getting more and more important. Regarding parameter optimization, much attention has been paid to Real-coded Genetic Algorithms (RCGA) because of their good searching ability and high flexibility. As for variable selection, traditionally Akaike Information Criterion (AIC) or Bayesian Information Criterion (BIC) are used quite often as selection criteria. These criteria estimate the relative quality of analysis models for a given set of data, but do not evaluate the importance of the variables themselves. This paper proposes a new variable selection method applying RCGA. This new variable selection method consists of 2 main components. The one is a new variable selection criterion utilizing the variances of genes in RCGA and the other is an estimation method of how far is in progress of RCGA optimization. The effectiveness of this new variable selection method is confirmed through application to a structural change model, which is one of discontinuous models. |
| Databáze: | OpenAIRE |
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