Abstract
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.
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Cornuéjols, A., Martin, C. (2011). Unsupervised Object Ranking Using Not Even Weak Experts. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24955-6_72
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DOI: https://doi.org/10.1007/978-3-642-24955-6_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24954-9
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