Evaluation Measures for Hierarchical Classification: a unified view and novel approaches

TL;DR

This paper analyzes existing hierarchical classification evaluation measures, proposes two novel generic evaluation approaches, and empirically demonstrates their advantages over current methods on large text datasets.

Contribution

It introduces two new hierarchical evaluation measures based on unified views, addressing limitations of existing metrics and improving evaluation consistency.

Findings

Existing measures exhibit undesirable evaluation behaviors.

Proposed measures outperform current methods in empirical tests.

New approaches provide more reliable hierarchical classification evaluation.

Abstract

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.