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Efficient genetic algorithm for feature selection for early time series classification
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DOI：10.1016/j.cie.2020.106345.png)
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摘要
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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.
Keyword:
Time series classification
Earliness
Feature selection
Genetic algorithm
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期刊
IF:
6.5
论文数:
1.0W
被引数:
3.8W
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