Conventional predictive Artificial Neural Networks (ANNs) commonly employ deterministic weight matrices; therefore, their prediction is a point estimate. Such a deterministic nature in ANNs causes the limitations of using ANNs for medical diagnosis, law problems, and portfolio management in which not only discovering the prediction but also the uncertainty of the prediction is essentially required. In order to address such a problem, we propose a predictive probabilistic neural network model, which corresponds to a different manner of using the generator in the conditional Generative Adversarial Network (cGAN) that has been routinely used for conditional sample genera-tion. By reversing the input and output of ordinary cGAN, the model can be successfully used as a predictive model; moreover, the model is robust against noises since adversarial training is employed. In addition, to measure the uncertainty of predictions, we introduce the entropy and relative entropy for regression problems and classification problems, respectively. The proposed framework is applied to stock market data and an image classification task. As a result, the proposed framework shows superior estimation performance, especially on noisy data; moreover, it is demonstrated that the proposed framework can properly estimate the uncertainty of predictions.
Bibliographical noteFunding Information:
Funding: This work was supported by grants from National Research Foundation of Korea (NRF-2019R1A2C1084778; NRF-2021R1F1A1050977) and Samsung Electronics (IO201214-08149-01).
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
- Adversarial learning
- Deep learning
- Generative adversarial network
- Portfolio management
- Probability estima-tion
- Risk estimation
ASJC Scopus subject areas
- Analytical Chemistry
- Information Systems
- Atomic and Molecular Physics, and Optics
- Electrical and Electronic Engineering