Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets
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 fo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2015-12-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://www.atlantis-press.com/article/25868655.pdf |
| _version_ | 1828531213410762752 |
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| author | Juan Luis Olmo Cristóbal Romero Eva Gibaja Sebastián Ventura |
| author_facet | Juan Luis Olmo Cristóbal Romero Eva Gibaja Sebastián Ventura |
| author_sort | Juan Luis Olmo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T22:34:26Z |
| format | Article |
| id | doaj.art-f8e22da3be814d3cadb219992a8d4829 |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-12-11T22:34:26Z |
| publishDate | 2015-12-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-f8e22da3be814d3cadb219992a8d48292022-12-22T00:48:02ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832015-12-018610.1080/18756891.2015.1113748Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data SetsJuan Luis OlmoCristóbal RomeroEva GibajaSebastián VenturaRecommending 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.https://www.atlantis-press.com/article/25868655.pdfMeta-learningMulti-label classificationEducational data miningStudents’ performance |
| spellingShingle | Juan Luis Olmo Cristóbal Romero Eva Gibaja Sebastián Ventura Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets International Journal of Computational Intelligence Systems Meta-learning Multi-label classification Educational data mining Students’ performance |
| title | Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets |
| title_full | Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets |
| title_fullStr | Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets |
| title_full_unstemmed | Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets |
| title_short | Improving Meta-learning for Algorithm Selection by Using Multi-label Classification: A Case of Study with Educational Data Sets |
| title_sort | improving meta learning for algorithm selection by using multi label classification a case of study with educational data sets |
| topic | Meta-learning Multi-label classification Educational data mining Students’ performance |
| url | https://www.atlantis-press.com/article/25868655.pdf |
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