Efficient training on biased minimax probability machine for imbalanced text classification

ABSTRACT

The Biased Minimax Probability Machine (BMPM) constructs a classifier which deals with the imbalanced learning tasks. In this paper, we propose a Second Order Cone Programming (SOCP) based algorithm to train the model. We outline the theoretical derivatives of the biased classification model, and address the text classification tasks where negative training documents significantly outnumber the positive ones using the proposed strategy. We evaluated the learning scheme in comparison with traditional solutions on three different datasets. Empirical results have shown that our method is more effective and robust to handle imbalanced text classification problems.

REFERENCES

• 1: Steven C. H. Hoi , Rong Jin , Michael R. Lyu, Large-scale text categorization by batch mode active learning, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135870]
• 2: Kaizhu Huang , Haiqin Yang , Irwin King , Michael R. Lyu , Laiwan Chan, The Minimum Error Minimax Probability Machine, The Journal of Machine Learning Research, 5, p.1253-1286, 12/1/2004

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

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

General Terms:
Algorithms, Experimentation, Management

Keywords:
biased classification, biased minimax probability machine, second order cone programming, text classification