Combining Classifiers based on Gaussian Mixtures
نویسنده
چکیده
A combination of classification rules (classifiers) is known as an Ensemble, and in general it is more accurate than the individual classifiers used to build it. Two popular methods to construct an Ensemble are Bagging (Bootstrap aggregating) introduced by Breiman, [4] and Boosting (Freund and Schapire, [11]). Both methods rely on resampling techniques to obtain different training sets for each of the classifiers. Previous work has shown that Bagging as well as Boosting are very effective for unstable classifiers. In this paper we present some results in application of Bagging and Boosting to classifiers where the class conditional density is estimated using Gaussian mixtures. The effect of feature selection on the Ensemble is also considered.
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