Group-based learning: a boosting approach

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.

• Type: Conference
• Year: 2008
• Where: CIKM
• Authors: Weijian Ni, Jun Xu, Hang Li, Yalou Huang
• Keywords: CIKM 2008, Group-based Learning, Group-based Measures, Information Management, Machine Learning Problems