Trinity College Dublin, Department of Computer Science
Tsymbal, Alexey. 'Feature Extraction for Dynamic Integration of Classifiers'. - Dublin, Trinity College Dublin, Department of Computer Science, TCD-CS-2006-32, 2006, pp27
Computer Science Technical Report TCD-CS-2006-32
Recent research has shown the integration of multiple classifiers to be one of the most
important directions in machine learning and data mining. 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. However, when dynamic
integration takes place in high dimensions the search for the neighborhood of a new instance is
problematic, since the majority of space is empty and neighbors can in fact be located far from
each other. Furthermore, when noisy or irrelevant features are present it is likely that also
irrelevant neighbors will be associated with a test instance. In this paper, we propose to use
feature extraction in order to cope with the curse of dimensionality in the dynamic integration
of classifiers. We consider classical principal component analysis and two eigenvector-based
class-conditional feature extraction methods that take into account class information.
Experimental results show that, on some data sets, the use of FEDIC leads to significantly
higher ensemble accuracies than the use of plain dynamic integration in the space of original
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