Name
Title | ||
|---|---|---|
Multiobjective optimization of ensembles of multilayer perceptrons for pattern classification |
Abstract | ||
|---|---|---|
Pattern classification seeks to minimize error of unknown patterns, however, in many real world applications, type I (false positive) and type II (false negative) errors have to be dealt with separately, which is a complex problem since an attempt to minimize one of them usually makes the other grow. Actually, a type of error can be more important than the other, and a trade-off that minimizes the most important error type must be reached. Despite the importance of type-II errors, most pattern classification methods take into account only the global classification error. In this paper we propose to optimize both error types in classification by means of a multiobjective algorithm in which each error type and the network size is an objective of the fitness function. A modified version of the GProp method (optimization and design of multilayer perceptrons) is used, to simultaneously optimize the network size and the type I and II errors. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11844297_46 | PPSN |
Keywords | Field | DocType |
multilayer perceptron,multiobjective optimization | Network size,Mathematical optimization,Parallel algorithm,Computer science,Multi-objective optimization,Fitness function,Multiobjective programming,Artificial intelligence,Perceptron,Machine learning | Conference |
Volume | ISSN | ISBN |
4193 | 0302-9743 | 3-540-38990-3 |
Citations | PageRank | References |
4 | 0.43 | 16 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. A. Castillo | 1 | 134 | 13.95 |
| Maribel García Arenas | 2 | 231 | 26.53 |
| J. J. Merelo | 3 | 363 | 33.51 |
| V. Rivas | 4 | 532 | 23.12 |
| Gustavo Romero | 5 | 197 | 23.79 |