Risk estimation for hierarchical classifier

Authors:
I. T. Podolak;A. Roman
Affiliations:
Institute of Computer Science, Jagiellonian University;Institute of Computer Science, Jagiellonian University
Venue:
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
Year:
2011

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Abstract

We describe the Hierarchical Classifier (HC), which is a hybrid architecture [1] built with the help of supervised training and unsupervised problem clustering. We prove a theorem giving the estimation R of HC risk. The proof works because of an improved way of computing cluster weights, introduced in this paper. Experiments show that R is correlated with HC real error. This allows us to use R as the approximation of HC risk without evaluating HC subclusters. We also show how R can be used in efficient clustering algorithms by comparing HC architectures with different methods of clustering.