Evaluation Measures for Hierarchical Classification: a unified view and novel approaches
This work addresses evaluation challenges for researchers and practitioners in hierarchical classification, offering incremental improvements to existing methods.
The paper tackles the problem of evaluating classification algorithms in hierarchical settings by analyzing existing measures and proposing two novel ones, with empirical tests on three large text classification datasets showing that the new methods overcome undesirable behaviors of existing approaches.
Hierarchical classification addresses the problem of classifying items into a hierarchy of classes. An important issue in hierarchical classification is the evaluation of different classification algorithms, which is complicated by the hierarchical relations among the classes. Several evaluation measures have been proposed for hierarchical classification using the hierarchy in different ways. This paper studies the problem of evaluation in hierarchical classification by analyzing and abstracting the key components of the existing performance measures. It also proposes two alternative generic views of hierarchical evaluation and introduces two corresponding novel measures. The proposed measures, along with the state-of-the art ones, are empirically tested on three large datasets from the domain of text classification. The empirical results illustrate the undesirable behavior of existing approaches and how the proposed methods overcome most of these methods across a range of cases.