Practical Evaluation of Online Heterogeneous Machine Learning

Kazuki SESHIMO  Akira OTA  Daichi NISHIO  Satoshi YAMANE  

IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.12   pp.2620-2631
Publication Date: 2020/12/01
Publicized: 2020/08/31
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2020EDP7020
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
machine-learning,  big data,  mixture model,  EM algorithm,  

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In recent years, the use of big data has attracted more attention, and many techniques for data analysis have been proposed. Big data analysis is difficult, however, because such data varies greatly in its regularity. Heterogeneous mixture machine learning is one algorithm for analyzing such data efficiently. In this study, we propose online heterogeneous learning based on an online EM algorithm. Experiments show that this algorithm has higher learning accuracy than that of a conventional method and is practical. The online learning approach will make this algorithm useful in the field of data analysis.