Dynamic Aspects of Statistical Classification

G. Nakhaeizadeh, C. C. Taylor, and G. Kunisch

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.


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