Fast classification using sparse decision DAGs

Abstract : In this paper we propose an algorithm that builds sparse decision DAGs (directed acyclic graphs) out of a list of base classifiers provided by an external learning method such as AdaBoost. The basic idea is to cast the DAG design task as a Markov decision process. Each instance can decide to use or to skip each base classifier, based on the current state of the classifier being built. The result is a sparse decision DAG where the base classifiers are selected in a data-dependent way. The method has a single hyperparameter with a clear semantics of controlling the accuracy/speed trade-off. The algorithm is competitive with state-of-the-art cascade detectors on three object-detection benchmarks, and it clearly outperforms them in the regime of low number of base classifiers. Unlike cascades, it is also readily applicable for multi-class classification. Using the multi-class setup, we show on a benchmark web page ranking data set that we can significantly improve the decision speed without harming the performance of the ranker.
Contributor : Marechal Françoise <>
Submitted on : Friday, June 22, 2012 - 3:00:12 PM
Last modification on : Friday, June 22, 2012 - 3:00:12 PM




  • HAL Id : in2p3-00711150, version 1



R. Busa-Fekete, D. Benbouzid, B. Kégl. Fast classification using sparse decision DAGs. J.Langford, J. Pineau. 29th International Conference on Machine Learning (ICML 2012), Jun 2012, Edinburgh, United Kingdom. Omnipress, pp.951-958. <in2p3-00711150>