Audit Quality from a Different Perspective: The Use of Machine Learning to Predict Restatements

Additional Metadata
Keywords	supervised machine learning; audit quality; restatements; undersampling; stratified k-fold cross validation; artificial neural network; k-nearest neighbour; decision tree; random forest; naïve Bayes; logistic regression; confusion matrix; accuracy; G-mean; AUC; ROC curve
Thesis Advisor	Bae, J.
Persistent URL	hdl.handle.net/2105/60720
Series	Business Economics
Organisation	Erasmus School of Economics
Citation APA APA Style APA-ALL Style AAA Style Cell Style Chicago Style Harvard Style IEEE Style MLA Style Nature Style Vancouver Style American-Institute-of-Physics Style Council-of-Science-Editors Style BibTex Format Endnote Format RIS Format CSL Format DOIs only Format	Dalebout, M.J. (2021). Audit Quality from a Different Perspective: The Use of Machine Learning to Predict Restatements. In Business Economics.http://hdl.handle.net/2105/60720

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