Hassan Najadat¹, Ibrahim Al-Daher², and Khaled Alkhatib¹

¹Computer Information Systems Department, Jordan University of Science and Technology, Jordan

²Computer Science Department, Jordan University of Science and Technology, Jordan

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.

Keywords: Data Envelopment Analysis, Decision Trees, Ensemble Methods, Financial Variables, Financial Ratios.

Received October 12, 2019; accepted April 8, 2020