The economics of classification: error vs. complexity

Abstract

Although usually classifier error is the main concern in publications, in real applications classifier evaluation complexity may play a large role as well. In the paper, a simple economic model is proposed with which a trade-off between classifier error and calculated evaluation complexity can be formulated. This trade-off can then be used to judge the necessity of increasing sample size or number of features to decrease classification error or, conversely, feature extraction or prototype selection to decrease evaluation complexity. The model is applied to the benchmark problem of handwritten digit recognition and is shown to lead to interesting conclusions, given certain assumptions.