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Learning Multiple Tasks with Boosted Decision Trees

Abstract : We address the problem of multi-task learning with no label correspondence among tasks. Learning multiple related tasks simultane- ously, by exploiting their shared knowledge can improve the predictive performance on every task. We develop the multi-task Adaboost en- vironment with Multi-Task Decision Trees as weak classifiers. We first adapt the well known decision tree learning to the multi-task setting. We revise the information gain rule for learning decision trees in the multi- task setting. We use this feature to develop a novel criterion for learning Multi-Task Decision Trees. The criterion guides the tree construction by learning the decision rules from data of different tasks, and representing different degrees of task relatedness. We then modify MT-Adaboost to combine Multi-task Decision Trees as weak learners. We experimentally validate the advantage of the new technique; we report results of ex- periments conducted on several multi-task datasets, including the Enron email set and Spam Filtering collection.
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Contributor : Rémi Gilleron Connect in order to contact the contributor
Submitted on : Tuesday, September 4, 2012 - 12:06:15 PM
Last modification on : Friday, November 18, 2022 - 9:24:26 AM


  • HAL Id : hal-00727749, version 1



Jean Baptiste Faddoul, Boris Chidlovskii, Rémi Gilleron, Fabien Torre. Learning Multiple Tasks with Boosted Decision Trees. ECML/PKDD - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - 2012, 2012, Bristol, United Kingdom. ⟨hal-00727749⟩



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