Feature Scoring by Mutual Information for Classification of Mass Spectra
نویسندگان
چکیده
Selecting relevant features in mass spectra analysis is important both for classification and search for causality. In this paper, it is shown how using mutual information can help answering to both objectives, in a model-free nonlinear way. A combination of ranking and forward selection makes it possible to select several feature groups that may lead to similar classification performances, but that may lead to different results when evaluated from an interpretability perspective.
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تاریخ انتشار 2006