An ensemble-based decision tree approach for educational data mining
Paper
Paper/Presentation Title | An ensemble-based decision tree approach for educational data mining |
---|---|
Presentation Type | Paper |
Authors | Abdar, Moloud (Author), Zomorodi-Moghadam, Mariam (Author) and Zhou, Xujuan (Author) |
Journal or Proceedings Title | Proceedings of the 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2018) |
Number of Pages | 4 |
Year | 2018 |
Place of Publication | Los Alamitos, CA, United States |
ISBN | 9781728102078 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/BESC.2018.00033 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/8697318 |
Conference/Event | 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2018) |
Event Details | 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC 2018) Parent International Conference on Behavioral, Economic and Socio-cultural Computing (BESC) Event Date 12 to end of 14 Nov 2018 Event Location Kaohsiung, Taiwan |
Abstract | 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. |
Keywords | educational data mining; data mining; ensembletechninuqe; rotaion forest algorithm; decision tree |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Byline Affiliations | University of Aizu, Japan |
Ferdowsi University of Mashhad, Iran | |
School of Management and Enterprise | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5112/an-ensemble-based-decision-tree-approach-for-educational-data-mining
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