Selecting Data Adaptive Learner from Multiple Deep Learners using Bayesian Networks

Autor:	Kobayashi, Shusuke, Shirayama, Susumu
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Statistics - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1007/s00521-020-05234-6
Popis:	A method to predict time-series using multiple deep learners and a Bayesian network is proposed. In this study, the input explanatory variables are Bayesian network nodes that are associated with learners. Training data are divided using K-means clustering, and multiple deep learners are trained depending on the cluster. A Bayesian network is used to determine which deep learner is in charge of predicting a time-series. We determine a threshold value and select learners with a posterior probability equal to or greater than the threshold value, which could facilitate more robust prediction. The proposed method is applied to financial time-series data, and the predicted results for the Nikkei 225 index are demonstrated. Comment: 14 pages, 12 tables and 4 figures, Submitted to Neural Computing and Applications
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2008.07709 Zobrazit plný text záznamu View this record from Arxiv