Group-based learning: a boosting approach

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.

REFERENCES

• 1: Thomas G. Dietterich , Richard H. Lathrop , Tomás Lozano-Pérez, Solving the multiple instance problem with axis-parallel rectangles, Artificial Intelligence, v.89 n.1-2, p.31-71, Jan. 1997 [doi>10.1016/S0004-3702(96)00034-3]
• 2: Thorsten Joachims, A support vector method for multivariate performance measures, Proceedings of the 22nd international conference on Machine learning, p.377-384, August 07-11, 2005, Bonn, Germany [doi>10.1145/1102351.1102399]
• 3: Robert E. Schapire , Yoram Singer, Improved Boosting Algorithms Using Confidence-rated Predictions, Machine Learning, v.37 n.3, p.297-336, Dec. 1999 [doi>10.1023/A:1007614523901]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Classifier design and evaluation

General Terms:
Algorithms, Experimentation, Theory

Keywords:
boosting, group-based learning