Tuning a multiple classifier system for side effect discovery using genetic algorithms
In previous work, a novel supervised framework implementing a binary classifier was presented that obtained excellent results for side effect discovery. Interestingly, unique side effects were identified when different binary classifiers were used within the framework, prompting the investigation of...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
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IEEE
2014
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| Online Access: | https://eprints.nottingham.ac.uk/3354/ |
| _version_ | 1848791021854392320 |
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| author | Reps, Jenna M. Aickelin, Uwe Garibaldi, Jonathan M. |
| author_facet | Reps, Jenna M. Aickelin, Uwe Garibaldi, Jonathan M. |
| author_sort | Reps, Jenna M. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | In previous work, a novel supervised framework implementing a binary classifier was presented that obtained excellent results for side effect discovery. Interestingly, unique side effects were identified when different binary classifiers were used within the framework, prompting the investigation of applying a multiple classifier system. In this paper we investigate tuning a side effect multiple classifying system using genetic algorithms. The results of this research show that the novel framework implementing a multiple classifying system trained using genetic algorithms can obtain a higher partial area under the receiver operating characteristic curve than implementing a single classifier. Furthermore, the framework is able to detect side effects efficiently and obtains a low false positive rate. |
| first_indexed | 2025-11-14T18:21:53Z |
| format | Conference or Workshop Item |
| id | nottingham-3354 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:21:53Z |
| publishDate | 2014 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-33542020-05-04T20:14:05Z https://eprints.nottingham.ac.uk/3354/ Tuning a multiple classifier system for side effect discovery using genetic algorithms Reps, Jenna M. Aickelin, Uwe Garibaldi, Jonathan M. In previous work, a novel supervised framework implementing a binary classifier was presented that obtained excellent results for side effect discovery. Interestingly, unique side effects were identified when different binary classifiers were used within the framework, prompting the investigation of applying a multiple classifier system. In this paper we investigate tuning a side effect multiple classifying system using genetic algorithms. The results of this research show that the novel framework implementing a multiple classifying system trained using genetic algorithms can obtain a higher partial area under the receiver operating characteristic curve than implementing a single classifier. Furthermore, the framework is able to detect side effects efficiently and obtains a low false positive rate. IEEE 2014-07 Conference or Workshop Item PeerReviewed Reps, Jenna M., Aickelin, Uwe and Garibaldi, Jonathan M. (2014) Tuning a multiple classifier system for side effect discovery using genetic algorithms. In: 2014 IEEE Congress on Evolutionary Computation (CEC), 6-11 July 2014, Beijing, China. adr Biomedical Informatics bradford hill ensemble http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6900328 |
| spellingShingle | adr Biomedical Informatics bradford hill ensemble Reps, Jenna M. Aickelin, Uwe Garibaldi, Jonathan M. Tuning a multiple classifier system for side effect discovery using genetic algorithms |
| title | Tuning a multiple classifier system for side effect discovery using genetic algorithms |
| title_full | Tuning a multiple classifier system for side effect discovery using genetic algorithms |
| title_fullStr | Tuning a multiple classifier system for side effect discovery using genetic algorithms |
| title_full_unstemmed | Tuning a multiple classifier system for side effect discovery using genetic algorithms |
| title_short | Tuning a multiple classifier system for side effect discovery using genetic algorithms |
| title_sort | tuning a multiple classifier system for side effect discovery using genetic algorithms |
| topic | adr Biomedical Informatics bradford hill ensemble |
| url | https://eprints.nottingham.ac.uk/3354/ https://eprints.nottingham.ac.uk/3354/ |