We propose a new nearest neighbor clustering classification algorithm based on fuzzy-rough set theory (FRNNC). First, we make every training sample fuzzy-roughness and use edit nearest neighbor algorithm to remove training sample points in class boundary or overlapping regions, and then use Mountain Clustering method to select representative cluster center points, then Fuzzy-Rough Nearest neighbor algorithm (FRNN) is applied to classify the test data. The new algorithm is applied to hand gesture image recognition, the results show that it is more effective and performs better than other nearest neighbor methods. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Wang, X., Yang, J., Teng, X., & Peng, N. (2006). Fuzzy-rough set based nearest neighbor clustering classification algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3613 LNAI, pp. 370–373). Springer Verlag. https://doi.org/10.1007/11539506_47
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