Algorithms for clustering data
Algorithms for clustering data
A new multi-layers method to analyze gene expression
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
A Fuzzy One Class Classifier for Multi Layer Model
WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Interval Length Analysis in Multi Layer Model
Computational Intelligence Methods for Bioinformatics and Biostatistics
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The paper describes an application of a one-class KNNto 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 KNNhas 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.