Group-based learning: a boosting approach

Authors:
Weijian Ni;Jun Xu;Hang Li;Yalou Huang
Affiliations:
Nankai University, Tianjin, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Nankai University, Tianjin, China
Venue:
Proceedings of the 17th ACM conference on Information and knowledge management
Year:
2008

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Abstract

This paper points out that many machine learning problems in IR should be and can be formalized in a novel way, referred to as 'group-based learning'. In group-based learning, it is assumed that training data as well as testing data consist of groups. The classifier is created and utilized across groups. Furthermore, evaluation in testing and also in training are conducted at group level, with the use of evaluation measures defined on a group. This paper addresses the problem and presents a Boosting algorithm to perform the new learning task. The algorithm, referred to as AdaBoost.Group, is proved to be able to improve accuracies in terms of group-based measures during training.