| Title |
A hybrid evolutionary approach to obtain better quality classifiers |
| Authors |
BECERRA ALONSO, DAVID, CARBONERO RUZ, MARIANO, MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ, MARTÍNEZ ESTUDILLO, ALFONSO CARLOS |
| External publication |
No |
| Means |
Lecture Notes in Computer Science |
| Scope |
Conference Paper |
| Nature |
Científica |
| JCR Quartile |
4 |
| SJR Quartile |
2 |
| SJR Impact |
0.338 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-79957941322&doi=10.1007%2f978-3-642-21498-1_21&partnerID=40&md5=04608fcd39348d6cbe275c58e2c77ac9 |
| Publication date |
01/01/2011 |
| Scopus Id |
2-s2.0-79957941322 |
| DOI |
10.1007/978-3-642-21498-1_21 |
| Abstract |
We present an extra measurement for classifiers, responding to the need to evaluate them with more than accuracy alone. This measure should be able to express, at least to some degree, the extent to which all classes are taken into account in a classification problem. In this communication we propose sensitivity dispersion (being as it is, the associated statistical dispersion measurement of accuracy), as the appropriate measure to have a more complete evaluation of the quality of classifiers. We use the Evolutionary Extreme Learning Machine algorithm, with a specific fitness function to optimize both measures simultaneously, and we compare it with other classifiers. © 2011 Springer-Verlag. |
| Keywords |
Evolutionary approach; Extreme learning machine; Fitness functions; Statistical dispersion; Evolutionary approach; Extreme learning machine; Fitness functions; Statistical dispersion; Dispersions; Neural networks; Dispersions; Learning systems; Neural networks; Quality control; Quality control |
| Universidad Loyola members |
|
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