An ensemble-based decision tree approach for educational data mining

Abdar, Moloud and Zomorodi-Moghadam, Mariam and Zhou, Xujuan (2018) An ensemble-based decision tree approach for educational data mining. In: 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2018), 12-14 Nov 2018, Kaohsiung, Taiwan.

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Nowadays, data mining and machine learning techniques are applied to a variety of different topics (e. g., healthcare and disease, security, decision support, sentiment analysis, education, etc.). Educational data mining investigates the performance of students and gives solutions to enhance the quality of education. The aim of this study is to use different data mining and machine learning algorithms on actual data sets related to students. To this end, we apply two decision tree methods. The methods can create several simple and understandable rules . Moreover, the performance of a decision tree is optimized by using an ensemble technique named Rotation Forest algorithm. Our findings indicate that the Rotation Forest algorithm can enhance the performance of decision trees in terms of different metrics. In addition, we found that the size of tree generated by decision trees ensemble were bigger than simple ones. This means that the proposed methodology can reveal more information concerning simple rules.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Accepted Version deposited in accordance with the copyright policy of the publisher.
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Management and Enterprise
Date Deposited: 02 May 2019 01:42
Last Modified: 24 Jun 2019 05:45
Uncontrolled Keywords: educational data mining; data mining; ensemble techninuqe; rotaion forest algorithm; decision tree
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1109/BESC.2018.00033

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