Majority Voting Combination of Multiple Classifiers for Bankruptcy Prediction
Publish Year: 1392
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICPEEE01_2135
تاریخ نمایه سازی: 16 شهریور 1395
Abstract:
The problem of bankruptcy prediction is one of the most actively studied nowadays. Many studies have been conducted on corporate bankruptcy prediction using data mining techniques. Artificial Neural Networks, Support Vector Machine and Decision Tree Algorithms are three current methods for data mining to prediction bankruptcy. This study puts forward a bankruptcy prediction methods based on majority voting combination of Artificial Neural Networks, Support Vector Machine and Decision Tree. Statistical population of this study includes 126 sound companies and 126 bankrupt companies, active in Tehran Stock Exchange Market between 2005 and 2011, which were studied for the three years of t , t-1 and t-2 . The results show that combination of relative majority voting with 92.20% accuracy in the year t and 88.88% accuracy in the year t-1 and 80.22% accuracy in the year t-2 is able to prediction corporate bankruptcy
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Authors
Adele Amini Salehi
Department of Accounting, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Hoda Majbouri Yazdi
Department of Accounting, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Yaser Hesari
Department of Accounting, Mashhad Branch, Islamic Azad University, Mashhad, Iran
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