AN EXTENSIVE EVALUATION OF DECISION TREE–BASED HIERARCHICAL MULTILABEL CLASSIFICATION METHODS AND PERFORMANCE MEASURES
Article first published online: 30 MAY 2013
© 2013 Wiley Periodicals, Inc.
How to Cite
Cerri, R., Pappa, G. L., Carvalho, A. C. P.L.F. and Freitas, A. A. (2013), AN EXTENSIVE EVALUATION OF DECISION TREE–BASED HIERARCHICAL MULTILABEL CLASSIFICATION METHODS AND PERFORMANCE MEASURES. Computational Intelligence. doi: 10.1111/coin.12011
- Article first published online: 30 MAY 2013
- Manuscript Accepted: 28 DEC 2012
- Manuscript Revised: 6 JUN 2012
- Manuscript Received: 31 MAR 2011
- performance measures;
- global and local approaches
Hierarchical multilabel classification is a complex classification problem where an instance can be assigned to more than one class simultaneously, and these classes are hierarchically organized with superclasses and subclasses, that is, an instance can be classified as belonging to more than one path in the hierarchical structure. This article experimentally analyses the behavior of different decision tree–based hierarchical multilabel classification methods based on the local and global classification approaches. The approaches are compared using distinct hierarchy-based and distance-based evaluation measures, when they are applied to a variation of real multilabel and hierarchical datasets' characteristics. Also, the different evaluation measures investigated are compared according to their degrees of consistency, discriminancy, and indifferency. As a result of the experimental analysis, we recommend the use of the global classification approach and suggest the use of the Hierarchical Precision and Hierarchical Recall evaluation measures.