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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Fernandez, J.C. Hervas, C. Martinez, F.J. Cruz, M. |
| Copyright Year | 2009 |
| Abstract | This paper proposes a multi-classification pattern algorithm using multilayer perceptron neural network models which try to boost two conflicting main objectives of a classifier, a high correct classification rate and a high classification rate for each class. To solve this machine learning problem, we consider a Memetic Pareto Evolutionary approach based on the NSGA2 algorithm (MPENSGA2), where we defined two objectives for determining the goodness of a classifier: the cross-entropy error function and the variation coefficient of its sensitivities, because both measures are continuous functions, making the convergence more robust. Once the Pareto front is built, we use an automatic selection methodology of individuals: the best model in accuracy (upper extreme in the Pareto front). This methodology is applied to solve six benchmark classification problems, obtaining promising results and achieving a high classification rate in the generalization set with an acceptable level of accuracy for each class. |
| Starting Page | 408 |
| Ending Page | 413 |
| File Size | 369804 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424447350 |
| DOI | 10.1109/ISDA.2009.153 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-11-30 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Algorithm design and analysis Machine learning algorithms Artificial neural networks Evolutionary computation Neural Networks Variation Coefficient Entropy Multi-objective Multi-layer neural network Convergence Neural networks Machine learning Classification Multilayer perceptrons |
| Content Type | Text |
| Resource Type | Article |
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