International Journal of Computational Intelligence Systems

Volume 8, Issue 6, December 2015, Pages 1144 - 1164

Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets

Authors
Juan Luis Olmo, Cristóbal Romero, Eva Gibaja, Sebastián Ventura
Corresponding Author
Juan Luis Olmo
Received 4 November 2014, Accepted 6 October 2015, Available Online 1 December 2015.
DOI
10.1080/18756891.2015.1113748How to use a DOI?
Keywords
Meta-learning, Multi-label classification, Educational data mining, Students’ performance
Abstract

Recommending classification algorithms is an open research problem the solution to which is of tremendous value for practitioners and non-experts data mining users such as educators. This paper proposes a new meta-learning framework for educational domains based on the use of multi-label learning for selecting the best classification algorithms in order to predict students’ performance. In short, the framework considers an offline phase where statistical tests are performed to find the subset of algorithms that achieves the best performance over the repository of educational data sets. The subset of algorithms along with the meta-features extracted from the training data are used to generate a multi-label data set. A multi-label classifier is then trained and, in an online phase, this model is used to recommend the most suitable classification algorithms to be applied to new unseen data sets. This new multi-label meta-learning approach has been applied to a repository of educational data sets generated from Moodle usage data. The results obtained show significant improvement compared with a previous nearest neighbor proposal, demonstrating the suitability of the new framework.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
8 - 6
Pages
1144 - 1164
Publication Date
2015/12/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2015.1113748How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Juan Luis Olmo
AU  - Cristóbal Romero
AU  - Eva Gibaja
AU  - Sebastián Ventura
PY  - 2015
DA  - 2015/12/01
TI  - Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets
JO  - International Journal of Computational Intelligence Systems
SP  - 1144
EP  - 1164
VL  - 8
IS  - 6
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2015.1113748
DO  - 10.1080/18756891.2015.1113748
ID  - Olmo2015
ER  -