Abstract
The paper describes an application of a one-class KNN to identify different signal patterns embedded in a noise structured background. The problem become harder whenever only one pattern is well represented in the signal, in such cases one class classifier techniques are more indicated. 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 one-class KNN has been tested on synthetic data that simulate microarray data for the identification of nucleosomes and linker regions across DNA. Results have shown a good recognition rate on synthetic data for nucleosome and linker regions.
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Di Gesù, V., Lo Bosco, G., Pinello, L. (2008). A One Class Classifier for Signal Identification: A Biological Case Study. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5179. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85567-5_93
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DOI: https://doi.org/10.1007/978-3-540-85567-5_93
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85566-8
Online ISBN: 978-3-540-85567-5
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