Hierarchical confusion matrix for classification performance evaluation

Abstract

This study proposes the novel concept of hierarchical confusion matrix, opening the door for popular confusion-matrix-based (flat) evaluation measures from binary classification problems, while considering the peculiarities of hierarchical classification problems. The concept is developed to a generalised form and proven its applicability to all types of hierarchical classification problems including directed acyclic graphs, multi-path labelling, and non-mandatory leaf-node prediction. Finally, measures based on the novel confusion matrix are used for three real-world hierarchical classification applications and compared to established evaluation measures. The results, the conformity with important attributes of hierarchical classification schemes and its broad applicability justify its recommendation.

Kevin Riehl

Doctoral Researcher & Scientist

My name is Kevin Riehl, and I am a cosmopolitan, technology enthusiast and philantrop. I believe, that technology is the key to make the world a better place, and that learning, self-improvement, collaboration and criticial thinking are our duty as gifted minds.