Optimizing Hierarchical Classification Through Tree Evolution

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.

Technical Report LU-CSE-11-002, Dept. of Computer Science and Engineering, Lehigh University, October, 2011.

An abbreviated version of this report was published as X. Qi and B. D. Davison. Hierarchy Evolution for Improved Classification. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management (CIKM), pages 2193-2196, Glasgow, Scotland, October 2011.

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