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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Villar, P. Fernandez, A. Herrera, F. |
| Copyright Year | 2011 |
| Description | Author affiliation: Dept. of Software Engineering, University of Granada, Granada, Spain (Villar, P.) || Dept. of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain (Herrera, F.) || Dept. of Computer Science, University of Jaén, Jaén, Spain (Fernandez, A.) |
| Abstract | This paper studies the behavior of a multiobjective Genetic Algorithm for jointly performing a feature selection and granularity learning for Fuzzy Rule-Based Classification Systems in the scenario of imbalanced data-sets. We refer to imbalanced data-sets when the class distribution is not uniform, a situation that it is present in many real application areas. We consider two different measures, one for the precision of the model and other for its complexity as the two objectives to optimize. In one previous approach, we aggregate these two measures in a single-objective Genetic Algorithm, and thus, a multiobjective approach of that Genetic Algorithm would yield a set of models with different trade-off between high accuracy and low complexity rather than a unique model, provided by the single-objective Genetic Algorithm. The experimental analysis, carried out over a wide range of imbalanced data-sets, shows that our approach is able to obtain a set of models with good trade-off between the two objectives considered but it is an open problem how to select the solution with best prediction ability from the whole set of solutions obtained. |
| Starting Page | 1239 |
| Ending Page | 1246 |
| File Size | 307312 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424473151 |
| ISSN | 10987584 |
| e-ISBN | 9781424473175 |
| e-ISBN | 9781424473168 |
| DOI | 10.1109/FUZZY.2011.6007436 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-27 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Accuracy Genetic algorithms Complexity theory Biological cells Training Predictive models Proposals granularity level Fuzzy Rule-Based Classification Systems imbalanced data-sets Multiobjective Genetic Algorithms feature selection |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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