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Wrapper filtering criteria via linear neuron and kernel approaches

Zervakis Michail, Μπλαζαντωνάκης Μιχάλης Ε.

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URI: http://purl.tuc.gr/dl/dias/AFC4CF9B-0EC4-4372-8285-746FB3A86870
Year 2008
Type of Item Peer-Reviewed Journal Publication
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Bibliographic Citation M. E. Blazadonakis and M. Zervakis, "Wrapper filtering criteria via linear neuron and kernel approaches," Computers Biol. Medi., vol. 38, no. 8, pp. 894-912, Aug. 2008. doi:10.1016/j.compbiomed.2008.05.005 https://doi.org/10.1016/j.compbiomed.2008.05.005
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Summary

The problem of marker selection in DNA microarray analysis has been addressed so far by two basic types of approaches, the so-called filter and wrapper methods. Wrapper methods operate in a recursive fashion where feature (gene) weights are re-evaluated and dynamically changing from iteration to iteration, while in filter methods feature weights remain fixed. Our objective in this study is to show that the application of filter criteria in a recursive fashion, where weights are potentially adjusted from cycle to cycle, produces noticeable improvement on the generalization performance measured on independent test sets.

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