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An Extensive Evaluation of Decision Tree–Based Hierarchical Multilabel Classification Methods and Performance Measures
Author(s) -
Cerri Ricardo,
Pappa Gisele L.,
Carvalho André Carlos P.L.F.,
Freitas Alex A.
Publication year - 2015
Publication title -
computational intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.353
H-Index - 52
eISSN - 1467-8640
pISSN - 0824-7935
DOI - 10.1111/coin.12011
Subject(s) - artificial intelligence , hierarchy , computer science , decision tree , consistency (knowledge bases) , machine learning , multi label classification , pattern recognition (psychology) , tree (set theory) , data mining , hierarchical clustering , class (philosophy) , mathematics , cluster analysis , mathematical analysis , economics , market economy
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.