JACIII Vol.25 No.2 pp. 153-161
doi: 10.20965/jaciii.2021.p0153


Optimal Value Estimation of Intentional-Value-Substitution for Learning Regression Models

Takuya Fukushima*, Tomoharu Nakashima*, Taku Hasegawa*, and Vicenç Torra**

*Osaka Prefecture University
1-1 Gakuen-cho, Naka-ku, Sakai, Osaka 599-8531, Japan

**Umeå University
MIT-huset, C444, Umeå universitet, Umeå 901 87, Sweden

March 18, 2020
November 25, 2020
March 20, 2021
missing values, neural networks, machine learning
Optimal Value Estimation of Intentional-Value-Substitution for Learning Regression Models

Intentional-value-substitution training

This paper focuses on a method to train a regression model from incomplete input values. It is assumed in this paper that there are no missing values in a training dataset while missing values exist during a prediction phase using the trained model. Under this assumption, we propose Intentional-Value-Substitution (IVS) training to obtain a machine learning model that makes the prediction error as minimum as possible. In IVS training, a model is trained to approximate the target function using a modified training dataset in which some feature values are substituted with a certain value even though their values are not missing. It is shown through a series of computational experiments that the substitution values calculated from a mathematical analysis help the models correctly predict outputs for inputs with missing values.

Cite this article as:
Takuya Fukushima, Tomoharu Nakashima, Taku Hasegawa, and Vicenç Torra, “Optimal Value Estimation of Intentional-Value-Substitution for Learning Regression Models,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.2, pp. 153-161, 2021.
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Last updated on Apr. 13, 2021