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 \(\hat{R}\) of HC risk. The proof works because of an improved way of computing cluster weights, introduced in this paper. Experiments show that \(\hat{R}\) is correlated with HC real error. This allows us to use \(\hat{R}\) as the approximation of HC risk without evaluating HC subclusters. We also show how \(\hat{R}\) can be used in efficient clustering algorithms by comparing HC architectures with different methods of clustering.
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Podolak, I.T., Roman, A. (2011). Risk Estimation for Hierarchical Classifier. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21219-2_21
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DOI: https://doi.org/10.1007/978-3-642-21219-2_21
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