| Title |
Evolutionary learning using a sensitivity-accuracy approach for classification |
| Authors |
SÁNCHEZ MONEDERO, JAVIER, Hervás-Martínez C. , MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, CARBONERO RUZ, MARIANO, Moreno M.C.R. , Cruz-Ramírez M. |
| External publication |
No |
| Means |
Lecture Notes in Computer Science |
| Scope |
Conference Paper |
| Nature |
Científica |
| JCR Quartile |
4 |
| SJR Quartile |
2 |
| SJR Impact |
0.322 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954603059&doi=10.1007%2f978-3-642-13803-4_36&partnerID=40&md5=147a8426ccabc6839d8d6047aa62c119 |
| Publication date |
01/01/2010 |
| Scopus Id |
2-s2.0-77954603059 |
| DOI |
10.1007/978-3-642-13803-4_36 |
| Abstract |
Accuracy alone is insufficient to evaluate the performance of a classifier especially when the number of classes increases. This paper proposes an approach to deal with multi-class problems based on Accuracy (C) and Sensitivity (S). We use the differential evolution algorithm and the ELM-algorithm (Extreme Learning Machine) to obtain multi-classifiers with a high classification rate level in the global dataset with an acceptable level of accuracy for each class. This methodology is applied to solve four benchmark classification problems and obtains promising results. © 2010 Springer-Verlag. |
| Keywords |
Benchmark classification; Classification rates; Data sets; Differential evolution algorithms; Evolutionary Learning; Extreme learning machine; Multi-class problems; Artificial intelligence; Classification (of information); Classifiers; Learning algorithms; Evolutionary algorithms |
| Universidad Loyola members |
|
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