Abstract
The performance of the Nearest Neighbor classifier drops significantly with the increase of the overlapping of the distribution of different classes. To overcome this drawback, we propose to simulate the physical process of gravitational collapse to trim the boundaries of the distribution of each class to reduce overlapping. The proposed simulated gravitational collapse(SGC) algorithm is tested on 7 real-world data sets. Experimental results show that the nearest prototype classifier based on SGC outperforms conventional NN and k-NN classifiers.
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Wang, C., Chen, Y.Q. (2005). Improving Nearest Neighbor Classification with Simulated Gravitational Collapse. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_104
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DOI: https://doi.org/10.1007/11539902_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
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