Publication: Improving deep learning performance with missing values via deletion and compensation
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2020-09
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Springer Nature
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Abstract
Missing values in a dataset is one of the most common difficulties in real applications. Many different techniques based on machine learning have been proposed in the literature to face this problem. In this work, the great representation capability of the stacked denoising auto-encoders is used to obtain a new method of imputating missing values based on two ideas: deletion and compensation. This method improves imputation performance by artificially deleting values in the input features and using them as targets in the training process. Nevertheless, although the deletion of samples is demonstrated to be really efficient, it may cause an imbalance between the distributions of the training and the test sets. In order to solve this issue, a compensation mechanism is proposed based on a slight modification of the error function to be optimized. Experiments over several datasets show that the deletion and compensation not only involve improvements in imputation but also in classification in comparison with other classical techniques.
Description
Proceedings of: International Work conference on the Interplay between Natural and Artificial Computation (IWINAC 2015)
Keywords
Missing values, Imputation, Classification, Deep learning
Bibliographic citation
Sánchez-Morales, A., Sancho-Gómez, J. L., Martínez-García, J. A. & Figueiras-Vidal, A. R. (2020). Improving deep learning performance with missing values via deletion and compensation. In: Neural Computing and Applications (Special Issue on Green and Human Information Technology 2019//International Work conference on the Interplay between Natural and Artificial Computation, IWINAC) 2015, 32(17), Sept. 2020, pp. 13233–13244.