Evaluation measures for hierarchical classification: a unified view and novel approaches

• DOI: 10.1007/s10618-014-0382-x
• Publication Date: 2014-09-05T13:03:16Z
• Authors: Aris Kosmopoulos; Ioannis Partalas; Eric Gaussier; Georgios Paliouras; Ion Androutsopoulos
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; 330; DAG-structured class hierarchies; Computer Science - Artificial Intelligence; Tree-structured class hierarchies; 02 engineering and technology; Hierarchical classification; 004; Machine Learning (cs.LG); Artificial Intelligence (cs.AI); [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]; 0202 electrical engineering, electronic engineering, information engineering; Evaluation measures; Evaluation

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.<br/>Submitted to journal<br/>