Machine Learning as an Experimental Science
Machine Learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Beautiful Evidence
Real-time ranking with concept drift using expert advice
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Ranking Electrical Feeders of the New York Power Grid
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
A process for predicting manhole events in Manhattan
Machine Learning
Machine Learning for theNew York City Power Grid
IEEE Transactions on Pattern Analysis and Machine Intelligence
COLT'05 Proceedings of the 18th annual conference on Learning Theory
U.S. grid gets less reliable [The Data]
IEEE Spectrum
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Ensuring reliability as the electrical grid morphs into the "smart grid" will require innovations in how we assess the state of the grid, for the purpose of proactive maintenance, rather than reactive maintenance; in the future, we will not only react to failures, but also try to anticipate and avoid them using predictive modeling (machine learning and data mining) techniques. To help in meeting this challenge, we present the Neutral Online Visualization-aided Autonomic evaluation framework (NOVA) for evaluating machine learning and data mining algorithms for preventive maintenance on the electrical grid. NOVA has three stages provided through a unified user interface: evaluation of input data quality, evaluation of machine learning and data mining results, and evaluation of the reliability improvement of the power grid. A prototype version of NOVA has been deployed for the power grid in New York City, and it is able to evaluate machine learning and data mining systems effectively and efficiently.