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ERIC Number: ED537217
Record Type: Non-Journal
Publication Date: 2012-Jun
Pages: 4
Abstractor: As Provided
Reference Count: 13
Meta-Learning Approach for Automatic Parameter Tuning: A Case Study with Educational Datasets
Molina, M. M.; Luna, J. M.; Romero, C.; Ventura, S.
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, Jun 19-21, 2012)
This paper proposes to the use of a meta-learning approach for automatic parameter tuning of a well-known decision tree algorithm by using past information about algorithm executions. Fourteen educational datasets were analysed using various combinations of parameter values to examine the effects of the parameter values on accuracy classification. Then, the new meta-dataset was used to predict the classification accuracy on the basis of the value parameters and some characteristics of the dataset. The obtained classification models can help us decide how the default parameters should be tuned in order to increase the accuracy of the classifier when using different types of educational datasets. (Contains 3 figures and 3 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (5th, Chania, Greece, June 19-21, 2012)," see ED537074.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Reports - Research; Speeches/Meeting Papers
Education Level: Higher Education; Postsecondary Education; Secondary Education
Audience: N/A
Language: English
Sponsor: Regional Government of Andalusia (Spain); Spanish Ministry of Science and Technology; FEDER (Fonds europeen de developpement regional) (France); Ministry of Education, Culture and Sport (Spain)
Authoring Institution: International Educational Data Mining Society
Identifiers - Location: Mexico; Spain