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

Paper:

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

Received:
March 18, 2020
Accepted:
November 25, 2020
Published:
March 20, 2021
Keywords:
missing values, neural networks, machine learning
Abstract

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.

Intentional-value-substitution training

Intentional-value-substitution training

Cite this article as:
T. Fukushima, T. Nakashima, T. Hasegawa, and V. 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.
Data files:
References
  1. [1] A. N. Baraldi, and C. K. Enders, “An introduction to modern missing data analyses,” J. of School Psychology, Vol.48, No.1, pp. 5-37, 2010.
  2. [2] R. J. A. Little and D. B. Rubin, “Statistical Analysis with Missing Data,” John Wiley & Sons, Inc., 1986.
  3. [3] D. B. Rubin, “Multiple Imputation For Nonresponse In Surveys,” Wiley Series in Probability and Statistics, 1987.
  4. [4] D. B. Rubin, “Multiple Imputation After 18+ Years,” J. of the American Statistical Association, Vol.91, No.434, pp. 473-489, 1996.
  5. [5] J. L. Schafer and J. W. Graham, “Missing data: our view of the state of the art,” J. of Psychological Methods, Vol.2, No.7, pp. 147-177, 2002.
  6. [6] V. Tresp, S. Ahmad, and R. Neuneier, “Training Neural Networks with Deficient Data,” Proc. of the 6th Int. Conf. on Neural Information Processing Systems (NIPS’93), pp. 128-135, 1993.
  7. [7] A. C. Acock, “Working With Missing Values,” J. of Marriage and Family, Vol.67, No.4, pp. 1012-1028, 2005.
  8. [8] T. Hasegawa, T. Fukushima, and T. Nakashima, “Robust Prediction against Missing Values by Intentional Value Substitution,” Proc. of 7th Int. Symp. on Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM’19), pp. 69-80, 2019.
  9. [9] D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” Proc of the 3rd Int. Conf. on Learning Representations (ICLR 2015), 2015.

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Last updated on Nov. 04, 2024