Multiobjective optimization for classifier ensemble and feature selection: an application to named entity recognition

Abstract

In this paper, the concept of finding an appropriate classifier ensemble for named entity recognition is posed as a multiobjective optimization (MOO) problem. Our underlying assumption is that instead of searching for the best-fitting feature set for a particular classifier, ensembling of several classifiers those are trained using different feature… (More)
DOI: 10.1007/s10032-011-0155-7

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