Interval Data Classification under Partial Information: A Chance-Constraint Approach

  • Sahely Bhadra
  • J. Saketha Nath
  • Aharon Ben-Tal
  • Chiranjib Bhattacharyya
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5476)


This paper presents a Chance-constraint Programming approach for constructing maximum-margin classifiers which are robust to interval-valued uncertainty in training examples. The methodology ensures that uncertain examples are classified correctly with high probability by employing chance-constraints. The main contribution of the paper is to pose the resultant optimization problem as a Second Order Cone Program by using large deviation inequalities, due to Bernstein. Apart from support and mean of the uncertain examples these Bernstein based relaxations make no further assumptions on the underlying uncertainty. Classifiers built using the proposed approach are less conservative, yield higher margins and hence are expected to generalize better than existing methods. Experimental results on synthetic and real-world datasets show that the proposed classifiers are better equipped to handle interval-valued uncertainty than state-of-the-art.


Gaussian Mixture Model Partial Information Synthetic Dataset Interval Data Second Order Cone Program 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sahely Bhadra
    • 1
  • J. Saketha Nath
    • 2
  • Aharon Ben-Tal
    • 2
  • Chiranjib Bhattacharyya
    • 1
  1. 1.Dept. of Computer Science and AutomationIndian Institute of ScienceBangaloreIndia
  2. 2.MINERVA Optimization centerFaculty of Industrial Engg. and Management, TechnionHaifaIsrael

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