The paper describes an application of a fuzzy one-class classifier (FOC) for the identification of different signal patterns embedded in a noise structured background. The classification phase is applied after a preprocessing phase based on a Multi Layer Model (MLM) that provides a preliminary signal segmentation in an interval feature space. The FOC has been tested on synthetic and real microarray data in the specific problem of DNA nucleosome and linker regions identification. Results have shown, in both cases, a good recognition rate.
Lo Bosco, G., Pinello, L. (2009). A Fuzzy One Class Classifier for Multi Layer Model. In V. Di Gesu, S.K. Pal, A. Petrosino (a cura di), Fuzzy Logic and Applications, 8th International Workshop, WILF 2009 Palermo, Italy, June 9-12, 2009 Proceedings (pp. 124-131) [10.1007/978-3-642-02282-1_16].
A Fuzzy One Class Classifier for Multi Layer Model
LO BOSCO, Giosue';PINELLO, Luca
2009-01-01
Abstract
The paper describes an application of a fuzzy one-class classifier (FOC) for the identification of different signal patterns embedded in a noise structured background. The classification phase is applied after a preprocessing phase based on a Multi Layer Model (MLM) that provides a preliminary signal segmentation in an interval feature space. The FOC has been tested on synthetic and real microarray data in the specific problem of DNA nucleosome and linker regions identification. Results have shown, in both cases, a good recognition rate.
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