In the past decades Machine Learning algorithms have been successfully used in numerous classification problems. While they usually significantly outperform domain experts (in terms of classification accuracy or otherwise), they are mostly not being used in practice. A plausible reason for this is that it is difficult to obtain an unbiased estimation of a single classification’s reliability. In the paper we propose a general transductive method for estimation of classification’s reliability on single examples that is independent of the applied Machine Learning algorithm. We compare our method with existing approaches and discuss its advantages. We perform extensive testing on 14 domains and 6 Machine Learning algorithms and show that our approach can frequently yield more than 100% improvement in reliability estimation performance.
CITATION STYLE
Kukar, M., & Kononenko, I. (2002). Reliable classifications with machine learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2430, pp. 219–231). Springer Verlag. https://doi.org/10.1007/3-540-36755-1_19
Mendeley helps you to discover research relevant for your work.