NDLI: The importance of the label hierarchy in hierarchical multi-label classification

Content Provider	Springer Nature Link
Author	Levatić, Jurica Kocev, Dragi Džeroski, Sašo
Copyright Year	2014
Abstract	We address the task of hierarchical multi-label classification (HMC). HMC is a task of structured output prediction where the classes are organized into a hierarchy and an instance may belong to multiple classes. In many problems, such as gene function prediction or prediction of ecological community structure, classes inherently follow these constraints. The potential for application of HMC was recognized by many researchers and several such methods were proposed and demonstrated to achieve good predictive performances in the past. However, there is no clear understanding when is favorable to consider such relationships (hierarchical and multi-label) among classes, and when this presents unnecessary burden for classification methods. To this end, we perform a detailed comparative study over 8 datasets that have HMC properties. We investigate two important influences in HMC: the multiple labels per example and the information about the hierarchy. More specifically, we consider four machine learning tasks: multi-label classification, hierarchical multi-label classification, single-label classification and hierarchical single-label classification. To construct the predictive models, we use predictive clustering trees (a generalized form of decision trees), which are able to tackle each of the modelling tasks listed. Moreover, we investigate whether the influence of the hierarchy and the multiple labels carries over for ensemble models. For each of the tasks, we construct a single tree and two ensembles (random forest and bagging). The results reveal that the hierarchy and the multiple labels do help to obtain a better single tree model, while this is not preserved for the ensemble models.
Starting Page	247
Ending Page	271
Page Count	25
File Format	PDF
ISSN	09259902
Journal	Journal of Intelligent Information Systems
Volume Number	45
Issue Number	2
e-ISSN	15737675
Language	English
Publisher	Springer US
Publisher Date	2014-12-04
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Predictive clustering trees Ensemble methods Hierarchical multi-label classification Habitat modelling Text classification Image classification Gene function prediction Information Storage and Retrieval Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) IT in Business Document Preparation and Text Processing
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture

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