Article: Solving a restriction of Bayesian network in giving domain knowledge by introducing factor nodes Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2022 Vol.20 No.4 pp.377 - 393 Abstract: Bayesian network is a probabilistic inference model that is effective for decision-making in business such as product development. Multiple events are represented as oval nodes and their relationships are drawn as edges among them. However, in order to obtain a sufficient effect, it is necessary to appropriately configure domain knowledge, for example more customer response to the product leads to more clarity of requirements for products. Such domain knowledge is configured as an edge connecting nodes. But in some cases, the constraint of the structure in a Bayesian network prevents this configuration. In this study, the authors propose a method to avoid this constraint by introducing the redundant factor nodes generated by applying factor analysis to the data related with domain knowledge. With this approach more domain knowledge can be applied to the Bayesian network, and the accuracy of decision-making in business is expected to be improved. Inderscience Publishers - linking academia, business and industry through research

Title: Solving a restriction of Bayesian network in giving domain knowledge by introducing factor nodes

Authors: Yutaka Iwakami; Hironori Takuma; Motoi Iwashita

Addresses: Department of Research and Analysis for IT Industry, Nork Research Co., Ltd., 2-13-10 Shinjuku, Shinjuku-Ku, Tokyo, 160-0022, Japan ' Department of Project Management, Chiba Institute of Technology, 2-17-1 Tsudanuma Narashino Chiba, 275-0016, Japan ' Department of Management Information Science, Chiba Institute of Technology, 2-17-1 Tsudanuma Narashino Chiba, 275-0016, Japan

Abstract: Bayesian network is a probabilistic inference model that is effective for decision-making in business such as product development. Multiple events are represented as oval nodes and their relationships are drawn as edges among them. However, in order to obtain a sufficient effect, it is necessary to appropriately configure domain knowledge, for example more customer response to the product leads to more clarity of requirements for products. Such domain knowledge is configured as an edge connecting nodes. But in some cases, the constraint of the structure in a Bayesian network prevents this configuration. In this study, the authors propose a method to avoid this constraint by introducing the redundant factor nodes generated by applying factor analysis to the data related with domain knowledge. With this approach more domain knowledge can be applied to the Bayesian network, and the accuracy of decision-making in business is expected to be improved.

Keywords: model improvement; data extraction; data driven insight; probabilistic inference; decision-making; product development; Bayesian network; factor analysis; key goal indicator; KGI; key performance indicator; KPI.

DOI: 10.1504/IJBIDM.2022.123217

International Journal of Business Intelligence and Data Mining, 2022 Vol.20 No.4, pp.377 - 393

Received: 15 Sep 2020
Accepted: 20 Nov 2020
Published online: 03 Jun 2022 *

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Title: Solving a restriction of Bayesian network in giving domain knowledge by introducing factor nodes

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