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Efficient genetic algorithm for feature selection for early time series classification

Title
Efficient genetic algorithm for feature selection for early time series classification
Author
허선
Keywords
Time series classification; Earliness; Feature selection; Genetic algorithm
Issue Date
2020-04
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Citation
COMPUTERS & INDUSTRIAL ENGINEERING, v. 142, Article no. 106345, 5pp
Abstract
This paper addresses a multi-objective feature selection problem for early time series classification. Previous research has focused on how many features to consider for a classifier, but has not considered the starting time of classification, which is also important for early classification. Motivated by this, we developed a mathematical model for which the objectives are to maximize classification performance and minimize the starting time and execution time of classification. We designed an efficient genetic algorithm to generate solutions with high probability. In experiment, we compared the proposed algorithm and general genetic algorithm under various experimental settings. From the experiment, we verified that the proposed algorithm can find a better feature set in terms of classification performance, starting time and execution time of classification than feature set found by general genetic algorithm.
URI
https://www.sciencedirect.com/science/article/pii/S0360835220300796https://repository.hanyang.ac.kr/handle/20.500.11754/164519
ISSN
0360-8352
DOI
10.1016/j.cie.2020.106345
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
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