Dynamic Aspects of Statistical Classification

Intelligent Adaptation

This paper discusses ideas for adaptive learning which can capture dynamic aspects of realworld datasets. Although some of these ideas have a general character and could be applied to any supervised algorithm, here we focus attention on a nearest neighbour classifier as well as linear, logistic and quadratic discriminant. The nearest neighbour classifier modifies the "training data" by keeping a record of usefulness as well as the age of the observation. The other classifiers use a quality control type-system to update rules appropriately. These methods are tried out on simulated data and real data from the credit industry.