Please use this identifier to cite or link to this item: https://doi.org/10.1109/CIS.2007.12
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dc.titleA constrained genetic algorithm for efficient dimensionality reduction for pattern classification
dc.contributor.authorPanicker, R.C.
dc.contributor.authorPuthusserypady, S.
dc.date.accessioned2014-06-19T02:52:54Z
dc.date.available2014-06-19T02:52:54Z
dc.date.issued2007
dc.identifier.citationPanicker, 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
dc.identifier.isbn0769530729
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/68760
dc.description.abstractIn 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/CIS.2007.12
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/CIS.2007.12
dc.description.sourcetitleProceedings - 2007 International Conference on Computational Intelligence and Security, CIS 2007
dc.description.page424-427
dc.identifier.isiut000247000000008
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