Classifier Monitoring using Statistical Tests

Summary

This paper is addressed to methods for early detection of classifier fall-down phenomenon, what gives a possibility to react in advance and avoid making incorrect decisions. For many applications it is very essential that decisions made by machine learning algorithms were as accurate as it is possible. The proposed approach consists in applying a monitoring mechanism only to results of classification, what not cause an additional computational over-head. The empirical evaluation of monitoring method is presented based on data extracted from simulated robotic soccer as an example of autonomous agent domain and synthetic data that stands for standard industrial application.