PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Learning Multiple Tasks with Boosted Decision Trees
Jean-Baptiste Faddoul, Boris Chidlovskii, Rémi Gilleron and Fabien Torre
In: ECML/PKDD - European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - 2012(2012).

Abstract

We address the problem of multi-task learning with no label correspondence among tasks. Learning multiple related tasks simultaneously, by exploiting their shared knowledge can improve the predictive performance on every task. We develop the multi-task Adaboost environment 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 experiments conducted on several multi-task datasets, including the Enron email set and Spam Filtering collection.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
Information Retrieval & Textual Information Access
ID Code:9635
Deposited By:Rémi Gilleron
Deposited On:09 December 2012