Το έργο με τίτλο Wrapper filtering criteria via linear neuron and kernel approaches από τον/τους δημιουργό/ούς Zervakis Michail, Μπλαζαντωνάκης Μιχάλης Ε. διατίθεται με την άδεια Creative Commons Αναφορά Δημιουργού 4.0 Διεθνές
Βιβλιογραφική Αναφορά
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
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.