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ROC Analysis and Cost-Sensitive Optimization for Hierarchical Classifiers

Instead of solving complex pattern recognition problems using a single complicated classifier, it is often beneficial to leverage our prior knowledge and decompose the problem into parts. These may be tackled using specific feature subsets and simpler classifiers resulting in a hierarchical system. In this paper, we propose an efficient and scalable approach for cost-sensitive optimization of a general hierarchical classifier using ROC analysis. This allows the designer to view the hierarchy of trained classifiers as a system, and tune it according to the application needs.