Confusion Matrix-Based Building of Hierarchical Classification
Present an evaluation of methods for automatically building hierarchical classifiers from the analysis on the confusion matrix generated with flat classification. By defining a basic framework for that, we investigate the effects of different methods for transforming the confusion matrix, for computing the similarity between classes, and the choice of base classifier. The experimental evaluation, conducted on three datasets from EMNIST with varied number of classes and samples, has shown that the choice of method can highly affect not only the overall accuracy of the system, but also the underlying hierarchical structure that is created. Among such methods, we demonstrate that the proposed penalty matrix with Pearson correlation as similarity metric might be the best option for finding confusion between the classes.
KeywordsClassification Hierarchical classification Confusion matrix
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