Bayesian network is one of major methods for probabilistic inference among items. But if it contains particular targeting node and other explanatory nodes for decision making, for example how to select suitable appealing keywords to make customers like a product, edges around the target should be counted with more importance than those among others while constructing the network. In order to achieve this adjustment, this study proposes to configure initial state consisting of a few nodes and their edges connected with the target. The initial state is obtained by leveraging Random forest which is a proven method for decision making. Initial nodes are extracted by measuring mean decrease of Gini coefficient calculated with decision trees of Random forest. Initial edges are designated by comparing Kullback-Leibler divergences of conditional probability distribution among nodes which are corresponding to edge directions. Through an actual experiment, this method is confirmed to be effective for adjusting Bayesian network in decision making. This approach is especially useful for business scenes, such as selecting preferable keywords for appealing products over SNS.
In product development, key performance indicators (KPIs) and key goal indicators (KGIs) have complex influences on each other. To understand the structure among them, Bayesian network analysis is one of effective methods. However, relationships among KPIs/KGIs often differ in attributes of enterprises, such as business type and annual sales. In this study, the authors incorporate topics obtained via latent Dirichlet allocation (LDA) into Bayesian network as nodes. With this “Bayesian network with topic nodes,” how KPIs affect the results of KGIs can be probabilistically inferenced and graphically observed according to attributes of enterprises. Furthermore, by configuring cultural or national differences as topic nodes, the proposed methods are expected to contribute overcoming barriers caused by these differences and accelerating improvement of product development in the global society.
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