Data partitioning evaluation measures for classifier ensembles

Authors:
Rozita A. Dara;Masoud Makrehchi;Mohamed S. Kamel
Affiliations:
Pattern Analysis and Machine Intelligence Laboratory, University of Waterloo, Waterloo, Ont., Canada;Pattern Analysis and Machine Intelligence Laboratory, University of Waterloo, Waterloo, Ont., Canada;Pattern Analysis and Machine Intelligence Laboratory, University of Waterloo, Waterloo, Ont., Canada
Venue:
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Year:
2005

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Abstract

Training data modification has shown to be a successful technique for the design of classifier ensemble. Current study is concerned with the analysis of different types of training set distribution and their impact on the generalization capability of multiple classifier systems. To provide a comparative study, several probabilistic measures have been proposed to assess data partitions with different characteristics and distributions. Based on these measures, a large number of disjoint training partitions were generated and used to construct classifier ensembles. Empirical assessment of the resulted ensembles and their performances have provided insights into the selection of appropriate evaluation measures as well as construction of efficient population of partitions.