Intelligent Sensor Data Analysis based on Cooperation of Knowledge Bases and Statistical Machine Learning

2020 Fiscal Year Final Research Report

• Project/Area Number: 19K21550
• Project/Area Number (Other): 18H06487 (2018)
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Multi-year Fund (2019); Single-year Grants (2018)
• Review Section: 1002:Human informatics, applied informatics and related fields
• Research Institution: Institute of Physical and Chemical Research
• Principal Investigator: Takeishi Naoya 国立研究開発法人理化学研究所, 革新知能統合研究センター, 特別研究員 (20824030)
• Project Period (FY): 2018-08-24 – 2021-03-31
• Keywords: 統計的機械学習 / 知識ベース / 事前知識 / 専門家知識 / センサデータ解析

Outline of Final Research Achievements

Statistial machine learning is a procedure to acquire (semi-)automatically a system that solves particular tasks with data of such tasks as input. Machine learning has been utilized in various tasks and data, but it is still challenging to interpret the results and to adapt to a small-data regime. We studied methodologies to incorpolate prior knowledge available as knowledge bases of application domains efficiently into machine learning. In particular, we focused on sensor data that often appear in engineering. We developed methods for incorporating prior knowledge, such as system diagrams (i.e., relation between feature of sensor data) and stability property of a system, into machine learning.

Free Research Field

機械学習

Academic Significance and Societal Importance of the Research Achievements

本研究では、センサデータ活用の場面で想定される形式の事前知識（システム図やシステムの安定性に関する知識）を機械学習に組み込む汎用的な方法を開発した。つまり、これまで利用することが難しかった、または利用するためには煩雑でアドホックな操作が必要だった事前知識を容易に機械学習で用いることができる。これにより、機械学習結果の効率や解釈性の向上が期待され、システム運用の場面で機械学習をさらに活用する助けになると期待できる。