Rapid Fine-Tuning of Computationally Intensive Classifiers

Abstract

This paper proposes a method for testing multiple parameter settings in one experiment, thus saving on computation-time. This is possible by simultaneously tracing processing for a number of parameters and, instead of one, generating many results – for all the variants. The multiple data can then be analyzed in a number of ways, such as by the binomial test used here for superior parameters detection. This experimental approach might be of interest to practitioners developing classifiers and fine-tuning them for particular applications, or in cases when testing is computationally intensive.