بررسی سودمندی انتخاب متغیرهای پیش‌بین در پیش‌بینی نوع اظهارنظر حسابرسان

Abbaszadeh, M. R. & Manzarzadeh, H. (2011). The effect of Characteristics of Board of Director on Reporting of Independent Auditors and Accepting Companies which Was Accepted in Tehran Stock Exchange, The Iranian Accounting and Auditing Review, 18 (63), 95-112.(in Persian)

Alfaro, E., García, N., Gámez, M. & Elizondo, D. (2008). Bankruptcy Forecasting: An Empirical Comparison of Adobos and Neural Networks, Decision Support Systems, 45 (1), 110-122.

Amini, P., Mohammadi, K. & Abbasi, SH. (2011). The investigation of effecting Factors on the issue of qualified auditing report: Application of neural network method, Journal of Management Accounting, 4(11), 25-39.
 (in Persian)

Ashbaugh, H. & Warfield, T.D. (2003). Audits as A Corporate Governance Mechanism: Evidence from The German Market. Journal of International Accounting Research, 2 (1), 1-21.

Bagherpoor Valashani, M., Saei, M., Meshkani, A. & Bagheri, M. (2013). Prediction of Independent Auditor Opinion in Iran: Data Mining Approach, Accounting and Auditing Research, 5 (19), 134-150.(in Persian)

Chen, C. P. & Zhao, R. (2000). An emerging market’s reaction to initial modified audit opinions: Evidence for the shanghai stock exchange. Contemporary Accounting Research, 17 (3), 429-555.

Chen, M.Y. (2011). Predicting Corporate Financial Distress based on Integration of Decision Tree Classification and Logistic Regression. Expert Systems with Applications, 38(9), 11261-11272.

DeAngelo, L. (1981). Auditor Size and Auditor Quality. Journal of Accounting and Economics, 3 (3), 183-199.

Dellepiane, U., Marcantonio, M., Laghi, E. & Renzi, S. (2015). Bankruptcy Prediction Using Support Vector Machines and Feature Selection during the Recent Financial Crisis. International Journal of Economics and Finance, 7 (8), 182-195.

Doumpos, M., Gaganis, C. & Pasiouras, F. (2005). Explaining qualifications in audit reports using a support vector machine methodology. Intelligent Systems in Accounting, Finance and Management, 13 (4), 197-215.

Fernandez-GaMez, M.A., Garcıa-Lagos, F. & Sanchez-Serrano, J.R. (2016). Integrating Corporate Governance and Financial Variables for the Identification of Qualified Audit Opinions with Neural Networks. Neural Computing and Applications, 27(5), 1-18.

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Gaganis, Ch., Pasiouras, F. & Doumpos, M. (2007). Probabilistic Neural Networks for the Identification of Qualified Audit Opinions, Expert Systems with Applications, 32 (1), 114-124.

Gaganis, C., Pasiouras, F., Spathis, C. & Zopounidis, C. (2007). A Comparison of Nearest Neighbours, Discriminant and Logit Models for Auditing Decisions, Intelligent Systems in Accounting, Finance and Management, 15 (1-2), 23-40.

Hall, M.A. & Smith, L.A. (1999). Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper. Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference, May 01 – 05, 235-239.

Hall, M. A. (2000). Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning. In Proceedings of the Seventeenth International Conference on Machine Learning, (June 29 - July 02), 359-366.

Hasas Yeghane, Y. & Yaghobi Manesh, S. (2003). The impact of audit reports on stock price. Accounting Empirical Studies, 1(3), 27-58.(in Persian)

Hasas Yeghane, Y., Taghavifard, M. & Mohammadpoor, F. (2014). Using Probabilistic Nueral Networks to Identifying. Auditing: Theory and Practice, 1 (1), 131-159.(in Persian)

Hejazi, R., Mohamadi, Sh., Aslani, Z. & Aghajan, M. (2012). Earnings Management Prediction Using Neural Networks and Decision Tree in TSE. The Iranian Accounting and Auditing Review, 19 (68), 31-46. (in Persian)

Hu, Y. C. (2010). Analytic Network Process for Pattern Classification Problems Using Genetic Algorithms. Information Sciences, 180(13), 2528–2539.

Jardin, P. (2010). Predicting Bankruptcy Using Neural Networks and Other Classification Methods: The Influence of Variable Selection Techniques on Model Accuracy, Neurocomputing, 73 (10-12), 2047-2060.

Karami, Gh. & Beik Boshroye, S. (2011). Corporate Governance and Equity Valuation: The Model by Using Artificial Neural Network, The Iranian Accounting and Auditing Review, 18 (64), 129-150.(in Persian)

Keasey, K., Watson, R. & Wynarzcyk, P. (1988). The small company audit qualification: a preliminary investigation. Accounting and Business Research, 18 (72), 323-333.

Kirkos, E., Spathis, C., Nanopoulos, A. & Manolopoulos, Y. (2007). Identifying Qualified Auditors opinion: A Data Mining Approach, Journal of Emerging technologies in Accounting, 4(1), 183-197.

Kohavi, R. (1995). A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence, 1137-1143.

Liang, D., Tsai, C.H. & Wu, H.T. (2015). The effect of feature selection on financial distress prediction. Knowledge-Based Systems, 73(1), 289-297.

Lo, S.C. (2010). The Effects of Feature Selection and Model Selection on the Correctness of Classification. Proceedings of the 2010 IEEE IEEM, 989-993.

Maggina, A. & Tsaklanganos, A. A. (2011). Predicting Audit Opinions Evidence from the Athens Stock Exchange. The Journal of Applied Business Research, 27 (4), 53-68.

Mahdavi, Gh. & Ghayori Moghadam, A. (2010). Investigating the information content of qualified audit reports in companies listed on Tehran Stock Exchange. Accounting and Auditing Research, 2(6), 65-85.(in Persian)

Menhaj, M. B. (1998). Foundations of Neural Networks. Tehran: Publication Center of Professor Hesabi.(in Persian)

Momeni, M. & Faal Ghayomi, A. (2007). Statistical Analysis Using SPSS (1^th ed). Tehran: New Book Publication.(in Persian)

Moradi, M. & Fakhrabadi, A. (2010). Evaluation of Cultural Factors Effect on Auditors Evaluation of Internal Control and Assess Control Risk, Financial Accounting Research, 1 (1-2), 89-102.(in Persian)

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Pourheidari, O. & Aazami, Z. (2010). Predicting Auditor’s Opinions: A Neural Networks Approach, Accounting Knowledge, 1(3), 77-97.(in Persian)

Ravi Kumar, P. & Ravi, V. (2007). Bankruptcy Prediction in Banks and Firms via Statistical and Intelligent Techniques - A Review, European Journal of Operational Research, 180 (1), 1-28.

Saeedi, A. & Aghaie, A. (2010). Predicting Financial Distress of firms Listed in Tehran Stock Exchange Using Bayesian networks. The Iranian Accounting and Auditing Review, 16 (3), 59-78.(in Persian)

Sajadi, H., Farazmand, H., Dastgir, M. & Dehghanfar, D. (2008). Factors affecting on qualified audit reports, Accounting Empirical Studies, (18), 123-145.
(in Persian)

Setayesh, M. & Jamalianpoor, M. (2009). The Investigating relationship between financial ratios and non-financial variables with the auditor's opinion. Accounting and Auditing Research, 1(2), 130-157.(in Persian)

Setayesh, M., Ebrahimi, F., Seif, M. & Sarikhani, M. (2013). Forecasting the Type of Audit Opinions: A Data Mining Approach. Journal of Management Accounting, 5 (4), 69-82.(in Persian)

Setayesh, M., Fatahi Nafchi, H., Abbaspoor, S. & Roustaei, M. (2014). Offering a new approach to the issuance of audit reports by using data mining (Case Study: Companies Listed in Tehran Stock Exchange). Journal of Audit Science, 14 (57), 5-26.(in Persian)

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Shourvarzi, M. R., Bakhtiyari, M., ZendehDel, A. & Esmaeilzadeh, M. (2011). Comparison of independent auditors and financial variables in predicting bankruptcy. The Iranian Accounting and Auditing Review, 18(65), 63-78.
(in Persian)

Tsai, C. (2009). Feature Selection in Bankruptcy Prediction. Knowledge Based Systems, 22 (2), 120-127.

Veerabhadrappa, J. & Rangarajan, L. (2010). Multi-Level Dimensionality Reduction Methods Using Feature Selection and Feature Extraction. International Journal of Artificial Intelligence and Applications, 1(4), 54-68.

Wang, G. Ma, J. & Yang, S. (2014). An improved boosting based on feature selection for corporate bankruptcy prediction. Expert Systems with Applications, 41 (5), 2353-2361.

Yu, L. & Liu, H. (2003). Feature Selection for High-Dimensional Data: A Fast Correlation-based Filter Solution. Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington DC, 856-863.