| Title |
Stochastic sensitivity analysis using extreme learning machine |
| Authors |
BECERRA ALONSO, DAVID, CARBONERO RUZ, MARIANO, MARTÍNEZ ESTUDILLO, ALFONSO CARLOS, MARTÍNEZ ESTUDILLO, FRANCISCO JOSÉ |
| External publication |
No |
| Means |
Adapt. Learn. Optim. |
| Scope |
Article |
| Nature |
Científica |
| SJR Quartile |
4 |
| SJR Impact |
0.14500 |
| Web |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959210968&doi=10.1007%2f978-3-319-04741-6_1&partnerID=40&md5=c1a23d76257320e9ee87cc6c130a1cc2 |
| Publication date |
01/01/2014 |
| Scopus Id |
2-s2.0-84959210968 |
| DOI |
10.1007/978-3-319-04741-6_1 |
| Abstract |
The Extreme Learning Machine classifier is used to perform the perturbative method known as Sensitivity Analysis. The method returns a measure of class sensitivity per attribute. The results show a strong consistency for classifiers with different random input weights. In order to present the results obtained in an intuitive way, two forms of representation are proposed and contrasted against each other. The relevance of both attributes and classes is discussed. Class stability and the ease with which a pattern can be correctly classified are inferred from the results. The method can be used with any classifier that can be replicated with different random seeds. © Springer International Publishing Switzerland 2014. |
| Keywords |
Classification; Elm feature space; Elm solutions space; Extreme learning machine; Sensitivity analysis; Stochastic classifiers |
| Universidad Loyola members |
|
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