Abstract
Nearest neighbor classifiers demand high computational resources, i.e., time and memory. Two distinct approaches are followed by researchers in pattern recognition to reduce this computational burden. The first approach is reducing the reference set (training set) and the second approach is dimensionality reduction which is referred to as prototype selection and feature reduction (a.k.a feature extraction or feature selection), respectively. In this paper, we cascaded the two methods to achieve the reduction in both directions. The experiments are done on the bench mark datasets, and the results obtained are satisfactory.
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Raja Kumar, R., Viswanath, P., Shobha Bindu, C. (2017). A Cascaded Method to Reduce the Computational Burden of Nearest Neighbor Classifier. In: Satapathy, S., Prasad, V., Rani, B., Udgata, S., Raju, K. (eds) Proceedings of the First International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 507. Springer, Singapore. https://doi.org/10.1007/978-981-10-2471-9_27
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DOI: https://doi.org/10.1007/978-981-10-2471-9_27
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