Parallel Multiobjective Optimization of Ensembles of Multilayer Perceptrons for pattern classification

@inproceedings{Castillo2006ParallelMO,
  title={Parallel Multiobjective Optimization of Ensembles of Multilayer Perceptrons for pattern classification},
  author={Pedro A. Castillo and Juan Juli{\'a}n Merelo Guerv{\'o}s},
  booktitle={Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial},
  year={2006}
}
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… CONTINUE READING

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