Parallel and local learning for fast probabilistic neural networks in scalable data mining

Probabilistic Neural Networks (PNN) represent knowledge in the form of simple well understood Bayesian models, which are most suitable for data mining applications that also need confidence levels. However if the number N of pattern neurons grows large, the evaluation of a single unknown sample has still O(N) complexity, which is not scale well. Parallelism can efficiently work towards speeding up the large neural networks. Whereas the PNN run time can be reduced by parallelism, its computational cost can be decreased by keeping only the globally most important pattern neurons. While such parsimonious global models have been studied for a long time, there is another way that is related to local learning algorithms. Maintain all N pattern neurons but to classify any unknown x use only the local ones, the k nearest neighbor to x neurons. This local learning PNN is investigated here. By using confidence ratio outputs we optimize the number of k nearest neighbor neurons for the best PNN performance. Combined with the parallel approach using p processors the method can reduce the cost of classifying an unknown x from O(N) to O(k/p).