Hierarchy Evolution for Improved Classification

Xiaoguang Qi and Brian D. Davison

Poster Paper (4 pages)
Official ACM published version: http://dx.doi.org/10.1145/2063576.2063924
Author's version: PDF (183KB)

Hierarchical classification has been shown to have superior performance than flat classification. It is typically performed on hierarchies created by and for humans rather than for classification performance. As a result, classification based on such hierarchies often yields suboptimal results. In this paper, we propose a novel genetic algorithm-based method on hierarchy adaptation for improved classification. Our approach customizes the typical GA to optimize classification hierarchies. In several text classification tasks, our approach produced hierarchies that significantly improved upon the accuracy of the original hierarchy as well as hierarchies generated by state-of-the-art methods.

In Proceedings of the 20th ACM Conference on Information and Knowledge Management (CIKM), pages 2193-2196, Glasgow, Scotland, October 2011. A longer version of this work is available as a technical report.

© ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.

The LSHTC dataset used in this paper comes from the LSHTC Pascal Challenge and can be downloaded here after registration.

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Last modified: 18 April 2013
Brian D. Davison