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Orthogonal variance decomposition based feature selection
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DOI：10.1016/j.eswa.2021.115191.png)
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Abstract
En 中文
Existing feature selection methods fail to properly account for interactions between features. In this paper, we attempt to remedy this issue by using orthogonal variance decomposition to evaluate features. The orthogonality of the decomposition allows us to directly calculate the total contribution of each feature to the output variance. As a result, we obtain an efficient and technically sound feature selection algorithm which takes into account feature interactions. The proposed algorithm has low computational complexity compared to other methods used in the literature. Numerical experiments demonstrate that our method accurately identifies relevant features and improves the accuracy of numerical models.
Keywords:
Feature selection
Variance decomposition
Sobol decomposition
Sensitivity index
Total sensitivity index
Wrapper methods
Data mining
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