In unsupervised ensemble learning, one obtains predictions from multiple sources or classifiers, yet without knowing the reliability and expertise of each source, and with no labeled data to assess it. The task is to combine these possibly conﬂicting predictions into an accurate meta-learner. Most works to date assumed perfect diversity between the different sources, a property known as conditional independence. In realistic scenarios, however, this assumption is often violated, and ensemble learners based on it can be severely sub-optimal. The key challenges we address in this paper are: (i) how to detect, in an unsupervised manner, strong violations of conditional independence; and (ii) construct a suitable meta-learner. To this end we introduce a statistical model that allows for dependencies between classifiers. Based on this model, we develop novel unsupervised methods to detect strongly dependent classifiers, better estimate their accuracies, and construct an improved meta-learner. Using both artificial and real datasets, we showcase the importance of taking classifier dependencies into account and the competitive performance of our approach.
|Original language||American English|
|Title of host publication||Proceedings of the 19th International Conference on Artificial Intelligence and Statistics|
|Editors||Arthur Gretton, Christian C. Robert|
|Place of Publication||Cadiz, Spain|
|Number of pages||10|
|State||Published - 1 Sep 2016|
|Name||Proceedings of Machine Learning Research|