Support vector machines, Decision Trees and Neural Networks for auditor selection

Issue title: Intelligent Systems and Knowledge Management

Guest editors: D.N. Kanellopoulos, S.B. Kotsiantis and P. Pintelas

Article type: Research Article

Authors: Kirkos, Efstathios^a | Spathis, Charalambos^b | Manolopoulos, Yannis^{c; *}

Affiliations: [a] Department of Accounting, Technological Educational Institution of Thessaloniki, PO BOX 141, 57400, Thessaloniki, Greece | [b] Division of Business Administration, Department of Economics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece | [c] Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Correspondence: [*] Corresponding author. Tel.: + 30 2310 991912; E-mail: manolopo@csd.auth.gr.

Abstract: The selection of a proper auditor is driven by several factors. Here, we use three data mining classification techniques to predict the auditor choice. The methods used are Decision Trees, Neural Networks and Support Vector Machines. The developed models are compared in term of their performances. The wrapper feature selection technique is used for the Decision Tree model. Two models reveal that the level of debt is a factor that influences the auditor choice decision. This study has implications for auditors, investors, company decision makers and researchers.

Keywords: Auditor choice, audit quality, Big 4 auditors, data mining, SVM, MLP, C4.5

DOI: 10.3233/JCM-2008-8305

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 8, no. 3, pp. 213-224, 2008