Dynamic Aspects of Statistical Classification
Gholamreza Nakhaeizadeh, G. Kunisch · 1996
This paper discusses ideas for adaptive learning which can capture dynamic aspects of real-world 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 "train-ing 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.