Scalable Optimization-Based Feature Selection Using Random Sampling
Date
Authors
Major Professor
Advisor
Committee Member
Journal Title
Journal ISSN
Volume Title
Publisher
Authors
Research Projects
Organizational Units
Journal Issue
Is Version Of
Versions
Series
Department
Abstract
We analyze an optimization-based approach called the NP-Filter for feature selection and show how the scalability of this method can be improved using random sampling of instances from the training data. The NP-Filter has attractive theoretical properties as the final solution quality can be quantified and it is flexible in terms of incorporating various feature evaluation methods. We show how the NP-Filter can automatically adjust to the randomness that occurs when a sample of training instances is used, and present numerical results that illustrate both this key result and the scalability improvement that are obtained.
Comments
This is a proceeding published as Yang, Jaekyung, and Sigurdur Olafsson. "Scalable Optimization-Based Feature Selection Using Random Sampling." In IIE Annual Conference. Proceedings, p. 1. Institute of Industrial and Systems Engineers (IISE), 2003. Posted with permission.