Abstract This study introduces an approach of combining Data Envelopment Analysis (DEA) and ensemble Methods in order to classify and predict the efficiency of Decision Making Units (DMU). The approach includes applying DEA in the first stage to compute the efficiency score for each DMU, then a variables’ ranker was utilized to extract the most important variables that affect the DMU’s performance, then J48 was adopted to build a classifier whose outcomes will be enhanced by Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples (DECORATE) Ensemble method. To examine the approach, this study utilizes a dataset from firms’ financial statements that are listed on Amman Stock Exchange. The dataset was preprocessed and turned out to include 53 industrial firms for the years 2012 to 2015.The dataset includes 11 input variables and 11 output ratios. The examination of financial variables and ratios play a vital role in the financial analysis practice. This paper shows that financial variable and ratio averages are points of reference to evaluate and measure firms’ future financial performance as well as that of other similar firms in the same sector. In addition, the results of this work are for comparative analyses of the financial performance of the industrial sector.

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[22] Zibanezhad E., Foroghi D., and Monadjemi A., “Applying Decision Tree To Predict Bankruptcy,” in Proceedings of IEEE International Conference on Computer Science and Automation Engineering, Shanghai, pp. 165- 169, 2011. Hassan Najadat is an associate professor of Computer Science. He works in the department of Computer Science and in the department of Computer Information Systems at Jordan University of Science and Technology, Irbid, Jordan. He earned his Ph.D. in Computer Science from North Dakota State University, ND, USA in 2005. His research interests include data mining, artificial intelligence, data science, and data envelopment analysis Ibrahim Al-Daher received his BSc degree in Computer Information Systems from Jordan University of science and Technology, Irbid, Jordan in 2013 and the MSc degree in Computer Science from the same university in 2018. He has worked as a Research and Teaching Assistant in Computer information Systems department, Jordan University of Science and Technology, Irbid, Jordan in 2014. His Research interests include data mining, machine learning, and data envelopment analysis. Khalid Alkhatib is an associate professor of accounting and finance at the department of Computer Information Systems at Jordan University of Science and Technology, Irbid, Jordan. He received his Ph.D. in accounting and finance, postgraduate diploma in social science research methods from Cardiff University/Wales, master’s degree in management and bachelor’s degree in business administration from the UK. His fields of interests are accounting information systems, technology and banking.