Bhadra, Sahely and Nath, J Saketha and Ben-Tal, Aharou and Bhattacharyya, Chiranjib (2009) Interval Data Classification under Partial Information: A Chance-Constraint Approach. In: Lecture Notes in Computer Science, 5476 . pp. 208-219.Full text not available from this repository. (Request a copy)
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.
|Item Type:||Journal Article|
|Additional Information:||Copyright of this article belongs to Springer.|
|Department/Centre:||Division of Electrical Sciences > Computer Science & Automation (Formerly, School of Automation)|
|Date Deposited:||26 Aug 2009 17:53|
|Last Modified:||26 Aug 2009 17:53|
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