Machine Learning
Robust Classification for Imprecise Environments
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Predicting good probabilities with supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Detecting statistical interactions with additive groves of trees
Proceedings of the 25th international conference on Machine learning
Pathfinder: an online collaboration environment for citizen scientists
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Contemporary Challenges in Ambient Data Integration for Biodiversity Informatics
OTM '09 Proceedings of the Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: ADI, CAMS, EI2N, ISDE, IWSSA, MONET, OnToContent, ODIS, ORM, OTM Academy, SWWS, SEMELS, Beyond SAWSDL, and COMBEK 2009
Exploring topic coherence over many models and many topics
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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The Cornell Laboratory of Ornithology's mission is to interpret and conserve the earth's biological diversity through research, education, and citizen science focused on birds. Over the years, the Lab has accumulated one of the largest and longest-running collections of environmental data sets in existence. The data sets are not only large, but also have many attributes, contain many missing values, and potentially are very noisy. The ecologists are interested in identifying which features have the strongest effect on the distribution and abundance of bird species as well as describing the forms of these relationships. We show how data mining can be successfully applied, enabling the ecologists to discover unanticipated relationships. We compare a variety of methods for measuring attribute importance with respect to the probability of a bird being observed at a feeder and present initial results for the impact of important attributes on bird prevalence.