A new classification rule based on nearest neighbour search

The nearest neighbour (NN) classification rule is usually chosen in a large number of pattern recognition systems due to its simplicity and good properties. As the problem of finding the nearest neighbour of an unknown sample is also of interest in other scientific communities (very large databases, data mining, computational geometry), a vast number of fast nearest neighbour search algorithms have been developed during the last years. In order to improve classification rates, the k-NN rule is often used instead of the NN rule, but it yields higher classification times. In this work we introduce a new classification rule applicable to many of those algorithms in order to obtain classification rates better than those of the nearest neighbour (similar to those of the k-NN rule) without significantly increasing classification time.