Ensemble learning with supervised kernels

Authors:
Kari Torkkola;Eugene Tuv
Affiliations:
Intelligent Systems Lab, Motorola, Tempe, AZ;Analysis and Control Technology, Intel, Chandler, AZ
Venue:
ECML'05 Proceedings of the 16th European conference on Machine Learning
Year:
2005

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Abstract

Kernel-based methods have outstanding performance on many machine learning and pattern recognition tasks. However, they are sensitive to kernel selection, they may have low tolerance to noise, and they can not deal with mixed-type or missing data. We propose to derive a novel kernel from an ensemble of decision trees. This leads to kernel methods that naturally handle noisy and heterogeneous data with potentially non-randomly missing values. We demonstrate excellent performance of regularized least square learners based on such kernels.