Please use this identifier to cite or link to this item:
|Title:||A constrained genetic algorithm for efficient dimensionality reduction for pattern classification|
|Authors:||Panicker, R.C. |
|Citation:||Panicker, R.C., Puthusserypady, S. (2007). A constrained genetic algorithm for efficient dimensionality reduction for pattern classification. Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007 : 424-427. ScholarBank@NUS Repository. https://doi.org/10.1109/CIS.2007.12|
|Abstract:||In automated pattern recognition systems, the two main challenges are feature selection and extraction. The features selected directly affects the number of measurements required; and extracting low-dimensional features from the selected ones reduces the computational complexity of the classifier. In traditional approaches, human expertise is obligatory for feature selection and statistical techniques are employed for feature projection. In this paper, a constrained genetic algorithm for performing these two tasks simultaneously, in conjunction with the k-nearest neighbor classifier is proposed. This algorithm requires minimal human intervention as it realizes good tradeoff solutions between classification accuracy, feature measurement requirements, and computational complexity. © 2007 IEEE.|
|Source Title:||Proceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Nov 11, 2018
checked on Sep 22, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.