A Dynamic Integration Algorithm with Ensemble of Classifiers
نویسندگان
چکیده
One of the most important directions in improvement of the datamining and knowledge discovery methods is the integration of the multiple classification techniques based on ensembles of classifiers. An integration technique should solve the problem of estimation and selection of the most appropriate component classifiers for an ensemble. We discuss an advanced dynamic integration of multiple classifiers as one possible variation of the stacked generalization method using the assumption that each component classifier is best inside certain areas of the application domain. In the learning phase a performance matrix of each component classifier is derived and then used in the application phase to predict performances of each component classifier with new instances.
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