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In this paper, we investigate the potentials of a novel classifier ensemble scheme, referred to as heterogeneous boosting (HB), which aims at delivering higher levels of diversity by allowing that distinct learning algorithms be recruited to induce the different components of the boosting sequence. For the automatic design of the HB structures in accord with the nuances of the problem at hand, a genetic...
Recent researches on feature selection have been conducted in an attempt to find efficient methods for automatic selection of relevant features. The idea is to select a subset of attributes which are as representative as possible of the original data. Committees of classifiers, also known as ensemble systems, are composed of individual classifiers, organized in a parallel way and their output are...
Bagging algorithm has been proven to be effective when dealing with on different classification problems. However, the success of Bagging depends strongly on the diversity level reached by the individual classifiers of the ensemble models. Diversity in ensemble can be obtained when the individual classifiers are built using different circumstances, such as parameter settings, training datasets and...
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