The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Constructing High Dimensional Feature Space for Time Series Classification
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Automatically finding the control variables for complex system behavior
Automated Software Engineering
A distributed hebb neural network for network anomaly detection
ISPA'07 Proceedings of the 5th international conference on Parallel and Distributed Processing and Applications
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We address the problem of identifying a non-redundant subset of important variables. All modern feature selection approaches including filters, wrappers, and embedded methods experience problems in very general settings with massive mixed-type data, and with complex relationships between the inputs and the target. We propose an efficient ensemble-based approach measuring statistical independence between a target and a potentially very large number of inputs including any meaningful order of interactions between them, removing redundancies from the relevant ones, and finally ranking variables in the identified minimum feature set. Experiments with synthetic data illustrate the sensitivity and the selectivity of the method, whereas the scalability of the method is demonstrated with a real car sensor data base.