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 Style AAA Style APA 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, December 9). Audit Quality from a Different Perspective: The Use of Machine Learning to Predict Restatements. Business Economics. Retrieved from http://hdl.handle.net/2105/60720

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