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Journal of Intelligent Information Systems

, Volume 52, Issue 1, pp 141–164 | Cite as

Improving large-scale hierarchical classification by rewiring: a data-driven filter based approach

  • Azad NaikEmail author
  • Huzefa Rangwala
Article

Abstract

Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down hierarchical classification with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i.e., defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art hierarchical classification approaches. Source Code: https://cs.gmu.edu/~mlbio/TaxMod/

Keywords

Top-down hierarchical classification Inconsistency Error propagation Flattening Clustering Rewiring 

Notes

Acknowledgements

The authors gratefully acknowledge support of the work described in this paper from the NSF grant #1447489 and #1252318.

Compliance with Ethical Standards

Conflict of interests

On behalf of all authors, the corresponding author states that there is no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Microsoft CorporationRedmondUSA
  2. 2.George Mason UniversityFairfaxUSA

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