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Saving Evaluation Time for the Decision Function in Boosting: Representation and Reordering Base Learner
Authors: Peng Sun and Jie Zhou
Conference: Proceedings of the 30th International Conference on Machine Learning (ICML-13)
Abstract: For a well trained Boosting classifier, we are interested in how to save the testing time, i.e., to make the decision without evaluating all the base learners. To address this problem, in previous work the base learners are sequentially calculated and early stopping is allowed if the decision function has been confident enough to output its value. In such a chain structure, the order of base learners is critical: better order can lead to less evaluation time. In this paper, we present a novel method for ordering. We base our discussion on the data structure representing Boosting's decision function. Viewing the decision function a boolean expression, we propose a Binary Valued Tree for its representation. As a secondary contribution, such a representation unifies the work by previous researchers and helps devise new representation. Also, its connection to Binary Decision Diagram(BDD) is discussed.
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