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Using Bayesian networks for bankruptcy prediction: Some methodological issues by Lili Sun; Prakash P. Shenoy is a Engineering article available to read on EtoBox.

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This study provides operational guidance for building naı ̈ve Bayes Bayesian network (BN) models for bankruptcy prediction. First, we suggest a heuristic method that guides the selection of bankruptcy predictors. Based on the correlations and partial correlations among variables, the method aims at eliminating redundant and less relevant variables. A naı ̈ve Bayes model is developed using the proposed heuristic method and is found to perform well based on a 10-fold validation analysis. The developed naı ̈ve Bayes model consists of eight first-order variables, six of which are continuous. We also provide guidance on building a cascaded model by selecting second-order variables to compensate for missing values of firstorder variables. Second, we analyze whether the number of states into which the six continuous variables are discretized has an impact on the model's performance. Our results show that the model's performance is the best when the number of states for discretization is either two or three. Starting from four states, the performance starts to deteriorate, probably due to over-fitting. Finally, we experiment whether modeling continuous variables with continuous distribu

Who reads Using Bayesian networks for bankruptcy prediction: Some methodological issues?

Author

Lili Sun; Prakash P. Shenoy

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0377-2217)

Published

2007

Language

EN

Field

Engineering (Physical Sciences)