Efficient pattern synthesis for nearest neighbour classifier

Synthetic pattern generation is one of the strategies to overcome the curse of dimensionality, but it has its own drawbacks. Most of the synthetic pattern generation techniques take more time than simple classification. In this paper, we propose a new strategy to reduce the time and memory requirements by applying prototyping as an intermediate step in the synthetic pattern generation technique. Results show that through the proposed strategy, classification can be done much faster without compromising much in terms of classification accuracy, in fact for some cases it gives better accuracy in lesser time. The classification time and accuracy can be balanced according to available memory and computing power of a system to get the best possible results.