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International Symposium on Knowledge and Systems Sciences

KSS 2018: Knowledge and Systems Sciences pp 142–157Cite as

Kansei Knowledge Extraction as Measure of Structural Heterogeneity

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 949))

Abstract

Representative measurements way of affective attributes is the Semantic Differential (SD) method in Kansei evaluation experiments. Structural heterogeneity of the subjective evaluations indicates the heterogeneous evaluation structure of each evaluator, which is presented individually by the specific selected factors. The objective of the structural heterogeneity modeling is not only to extract the general trends but also to identify the diverse individual evaluation structure. In this paper, we propose a Hierarchical Bayes Regression model with Heterogeneous Variable Selection (HBRwHVS), which simultaneously analyses the individual models and identifies the influential explanatory variables based on the framework of the Hierarchical Bayes Regression Modeling. The results offer the relational data between the evaluators and the selected items as carefully chosen explanatory variables. We apply the proposed method to the analysis of sensibility subjective evaluation data on traditional craft and show its effectiveness. After obtaining the estimated values of model parameters, cluster analysis is performed on subjects with the similar evaluation structures as another example of its applications.

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Acknowledgment

This work is supported by JSPS Grand-in-Aid for Scientific Research(B)18H00904.

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Authors and Affiliations

  1. Faculty of Business Sciences, University of Tsukuba, Tokyo, Japan

    Mina Ryoke & Tadahiko Sato

Authors
  1. Mina Ryoke
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  2. Tadahiko Sato
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Corresponding author

Correspondence to Mina Ryoke .

Editor information

Editors and Affiliations

  1. School of Economics and Management, Tsinghua University, Beijing, China

    Jian Chen

  2. Graduate School of Business Sciences, University of Tsukuba, Bunkyō, Tokyo, Tokyo, Japan

    Yuji Yamada

  3. Faculty of Business Sciences, University of Tsukuba, Tokyo, Japan

    Mina Ryoke

  4. Institute of Systems Science, CAS Academy of Mathematics and Systems Sciences, Beijing, China

    Xijin Tang

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Ryoke, M., Sato, T. (2018). Kansei Knowledge Extraction as Measure of Structural Heterogeneity. In: Chen, J., Yamada, Y., Ryoke, M., Tang, X. (eds) Knowledge and Systems Sciences. KSS 2018. Communications in Computer and Information Science, vol 949. Springer, Singapore. https://doi.org/10.1007/978-981-13-3149-7_11

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  • DOI: https://doi.org/10.1007/978-981-13-3149-7_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3148-0

  • Online ISBN: 978-981-13-3149-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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