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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hai Thanh Nguyen Franke, K. Petrovic, S. |
| Copyright Year | 2010 |
| Abstract | Performance of a pattern recognition system depends strongly on the employed feature-selection method. We perform an in-depth analysis of two main measures used in the filter model: the correlation-feature-selection (CFS) measure and the minimal-redundancy-maximal-relevance (mRMR) measure. We show that these measures can be fused and generalized into a generic feature-selection (GeFS) measure. Further on, we propose a new feature-selection method that ensures globally optimal feature sets. The new approach is based on solving a mixed 0-1 linear programming problem (M01LP) by using the branch-and-bound algorithm. In this M01LP problem, the number of constraints and variables is linear ($O(n)$) in the number $n$ of full set features. In order to evaluate the quality of our GeFS measure, we chose the design of an intrusion detection system (IDS) as a possible application. Experimental results obtained over the KDD Cup'99 test data set for IDS show that the GeFS measure removes 93% of irrelevant and redundant features from the original data set, while keeping or yielding an even better classification accuracy. |
| Starting Page | 1529 |
| Ending Page | 1532 |
| File Size | 427334 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424475421 |
| ISSN | 10514651 |
| e-ISBN | 9781424475414 |
| DOI | 10.1109/ICPR.2010.378 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-23 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Polynomials Feature extraction Accuracy Intrusion detection Correlation Computational modeling Mutual information |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition |
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