Improving classifier accuracy by simulating fuzzy boundaries between classes

The pattern classification problem can be defined as one of assigning a label to a pattern of unknown class based on labelled prototype patterns. The method described in this paper is based on the following two ideas which appeal to our common sense: when the correctness of a classifier on a pattern x is in question, it is best to consider the performance of the same classifier on the patterns which are similar to x; and a classifier is usually accurate when the test pattern x falls close to the center of its class in feature space and prone to error when it falls near a class boundary.