Heterogeneous Ensemble Classification
نویسندگان
چکیده
The problem of multi-class classification is explored using heterogeneous ensemble classifiers. Heterogeneous ensembles classifiers are defined as ensembles, or sets, of classifier models created using more than one type of classification algorithm. For example, the outputs of decision tree classifiers could be combined with the outputs of support vector machines (SVM) to create a heterogeneous ensemble. We explore how, when, and why heterogeneous ensembles should be used over other classification methods. Specifically we look into the use of bagging and different fusion methods for heterogeneous and homogeneous ensembles. We also introduce the Hemlock framework, a software tool for creating and testing heterogeneous ensembles.
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