A new class of edge-preserving smoothing filters
Pattern Recognition Letters
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A Further Comparison of Splitting Rules for Decision-Tree Induction
Machine Learning
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
The Labeled Cell Classifier: A Fast Approximation to k Nearest Neighbors
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
An Algorithm for Finding Nearest Neighbors
IEEE Transactions on Computers
An average-case analysis of the k-nearest neighbor classifier for noisy domains
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Combining Local and Global KNN With Cotraining
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
| Hi-index | 0.00 |
In this paper, we propose a thorough investigation of a nearest neighbor rule which we call the "Symmetric Nearest Neighbor (sNN) rule". Basically, it symmetrises the classical nearest neighbor relationship from which are computed the points voting for some instance. Experiments on 29 datasets, most of which are readily available, show that the method significantly outperforms the traditional Nearest Neighbors methods. Experiments on a domain of interest related to tropical pollution normalization also show the greater potential of this method. We finally discuss the reasons for the rule's efficiency, provide methods for speeding-up the classification time, and derive from the sNN rule a reliable and fast algorithm to fix the parameter k in the k-NN rule, a longstanding problem in this field.