Abstract
A feature synthesis algorithm combined with modified k-NN classifier is described in this paper. The feature information of the training data is extracted firstly. In the classification phase, the feature information of the training data and the testing data are compared to make the initial prediction. If the predicting result is {C} which has only one element, testing data will be labeled class C. If the predicting result is {C1, C2, ...Ci}, the corresponding subset of the training data set will be used in the k-NN algorithm to attain the predicted result. It is shown that this algorithm combined k-NN rule reduces computing time. If the feature information set could well generalize the training data, the error rate of the algorithm is decreased. The effectiveness of this proposed approach is verified on a public remote sensing data set.
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Hu, Z., Cai, Z. (2010). Feature Synthesis Algorithm Combined with k-NN Classifier for Spectral Data Classification. In: Cai, Z., Tong, H., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2010. Communications in Computer and Information Science, vol 107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16388-3_28
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DOI: https://doi.org/10.1007/978-3-642-16388-3_28
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