Orthogonal variance decomposition based feature selection

Kamalov, Firuz
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Elsevier Ltd
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. © 2021 Elsevier Ltd
This article is not available at CUD collection. The version of scholarly record of this article is published in Expert Systems with Applications (2021), available online at: https://doi.org/10.1016/j.eswa.2021.115191
Data mining, Feature selection, Sensitivity index, Sobol decomposition, Total sensitivity index, Variance decomposition, Wrapper methods
Kamalov, F. (2021). Orthogonal variance decomposition based feature selection. Expert Systems with Applications, 182 https://doi.org/10.1016/j.eswa.2021.115191