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大学・研究所にある論文を検索できる 「マルチドメイン空間における機械学習を用いたバリュエーションモデルの高度化に関する研究」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

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マルチドメイン空間における機械学習を用いたバリュエーションモデルの高度化に関する研究

岩井 康一 横浜国立大学 DOI:info:doi/10.18880/00014608

2022.05.26

概要

A valuation model is a workflow to assess the value of the things such as company, collateral, and credit. In the recent progress of information technology, the complexity and speed to change were drastically increased and it has been getting harder to see the values of things properly and correctly. The valuation model is now very keen to be improved and evolved against the complicated domain data, especially by using machine learning. However, the conventional approaches of machine learning do not perform well in many cases. The traditional approach requires the target data is a single domain, which consists of uniform and similar data. In a recent trend, on the other hand, it could not define the single domain as the data get so much complicated and it cannot avoid containing varieties of data. In this research, we discuss how to progress the valuation model to adapt to the situation, i.e. multi-domain spaces. An approach in this paper is domain classification. If the target domain data is complex enough to execute the traditional machine learning, it would be straightforward to try domain classification and segregation to fit the purpose of the model. However, this is even difficult that in the valuation model not only the features of the data but also marketability affects the domain classification and result of the predictions. We discuss an approach using the multiagent scheme to segregate the domain from others and extract knowledge from the domain to utilize them for predictions. Another subject is transfer learning. Transfer learning is a methodology to utilize knowledge from learned domain space to unlearned domain space. Especially valuation model has the problem that it takes huge cost and effort to get supervised data in a short term period as the value of things would be only revealed after a certain period. In this paper, we propose a transfer learning method using a Bayesian network to extract knowledge from the graph that consists of features of domain data and utilize them for machine learning. These contributions make the valuation model an advanced and generic method for machine learning in multi-domain spaces.

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