Dynamic Integration of Classifiers in the Space of Principal Components

Recent research has shown the integration of multiple classifiers to be one of the most important directions in machine learning and data mining. It was shown that, for an ensemble to be successful, it should consist of accurate and diverse base classifiers. However, it is also important that the integration procedure in the ensemble should properly utilize the ensemble diversity. In this paper, we present an algorithm for the dynamic integration of classifiers in the space of extracted features (FEDIC). It is based on the technique of dynamic integration, in which local accuracy estimates are calculated for each base classifier of an ensemble, in the neighborhood of a new instance to be processed. Generally, the whole space of original features is used to find the neighborhood of a new instance for local accuracy estimates in dynamic integration. In this paper, we propose to use feature extraction in order to cope with the curse of dimensionality in the dynamic integration of classifi...

• Type: Conference
• Year: 2003
• Where: ADBIS
• Authors: Alexey Tsymbal, Mykola Pechenizkiy, Seppo Puuronen, David W. Patterson
• Keywords: ADBIS 2003, Database, Dynamic Integration, Local Accuracy Estimates, Plain Dynamic Integration