A new probabilistic active sample selection algorithm for class imbalance problem
by T. Maruthi Padmaja; Bapi S. Raju; P. Radha Krishna
International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP), Vol. 4, No. 1, 2013

Abstract: The performance of the support vector machine classification model is prone to the class imbalance problem, which occurs when one class of data severely outnumbers the other class. Traditionally, this issue could be addressed by balancing class distributions with sampling methods. This paper explores and applies the probabilistic active learning (StatQSVM) (Mitra et al., 2004) strategy for yielding balanced class distributions from large scale unbalanced datasets. Rather than querying the instances based on their proximity, StatQSVM selects a set of instances based on locally defined confidence factor with respect to current hyperplane that models the class separation. The explorative study on StatQSVM is carried out using simulated as well as real-world unbalanced datasets. Performance deterioration was observed at high class imbalance settings within the study. To overcome this problem, a fast probabilistic cost weighted undersampling approach, called CStatQSVM with a new stopping criterion is proposed. The experimental results show that the CStatQSVM is successful on minority as well as majority class prediction as compared to LOB, StatQSVM active learning methods and other conventional methods that address class imbalance problem.

Online publication date: Sat, 19-Jul-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Knowledge Engineering and Soft Data Paradigms (IJKESDP):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com