Fast Density Estimation for Approximated k Nearest Neighbor Classification

We propose a method for fast density estimation of samples, which makes it possible to significantly accelerate classification based on the k nearest neighbor (kNN) method. Our main premise is that many trials of a rough estimation of probability density function are conducted, and they are integrated by Bayes' theorem. The experimental results indicated that the classification time used in our method was at least 30 times faster than that of kNN.