Online estimation of discrete densities using classifier chains
Geilke, M., Frank, E., & Kramer, S. (2012). Online estimation of discrete densities using classifier chains. In Proceedings of ECML PKDD 2012 Workshop on Instant Interactive Data Mining, Bristol, UK, 24-28 September 2012.
Permanent Research Commons link: https://hdl.handle.net/10289/8421
We propose an approach to estimate a discrete joint density online, that is, the algorithm is only provided the current example, its current estimate, and a limited amount of memory. To design an online estimator for discrete densities, we use classifier chains to model dependencies among features. Each classifier in the chain estimates the probability of one particular feature. Because a single chain may not provide a reliable estimate, we also consider ensembles of classifier chains. Our experiments on synthetic data show that the approach is feasible and the estimated densities approach the true, known distribution with increasing amounts of data.
This is an author’s accepted version of an article published in Proceedings of ECML PKDD 2012 Workshop on Instant Interactive Data Mining.