A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
An efficient boosting algorithm for combining preferences
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Many problems, like feature selection, involve evaluating objects while ignoring the relevant underlying properties that determine their true value. Generally, an heuristic evaluating device (e.g. filter, wrapper, etc) is then used with no guarantee on the result. We show in this paper how a set of experts (evaluation function of the objects), not even necessarily weakly positively correlated with the unknown ideal expert, can be used to dramatically improve the accuracy of the selection of positive objects, or of the resulting ranking. Experimental results obtained on both synthetic and real data confirm the validity of the approach. General lessons and possible extensions are discussed.