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Appraisal of companies with Bayesian networks
Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, Tokyo, Japan.ORCID iD: 0000-0003-1654-9148
Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, Tokyo, Japan.
Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, Tokyo, Japan.
2006 (English)In: International Journal of Business Intelligence and Data Mining, ISSN 1743-8187, Vol. 1, no 3, 329-346 p.Article in journal (Refereed) Published
Abstract [en]

Appraisal of companies is an important business activity. We mainly apply Bayesian networks for this classification task for Japanese electric company data. Firstly, few standard statistical techniques are performed. Then Bayesian networks are applied in four steps: (1) for implementing a current procedure of economical experts, where economical variables are clustered and then summarised for computing a score for deciding the economical state of the company, (2) the same is done but with clustering of economical variables based on data, (3) the naive Bayes classifier and (4) an improved naive Bayes classifier through adjusting its conditional density of each feature variable given the class variable, which are initially obtained by maximum likelihood estimation. Adjustments are done by using the simulated annealing optimisation. Finally, a sensible way for appraisal of companies is discussed.

Place, publisher, year, edition, pages
InderScience Publishers, 2006. Vol. 1, no 3, 329-346 p.
Keyword [en]
dependence, naive Bayes assumption, Bayesian networks, probability adjustment, classification accuracy, company appraisal, Japan, electric companies, economic variables, simulated annealing; optimisation, credit rating; company performance; company evaluation
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-128741DOI: 10.1504/IJBIDM.2006.009138OAI: oai:DiVA.org:umu-128741DiVA: diva2:1056036
Available from: 2016-12-13 Created: 2016-12-13 Last updated: 2017-02-07

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Wijayatunga, Priyantha
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Citation style
  • apa
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