6533b7d1fe1ef96bd125cd41

RESEARCH PRODUCT

A one class KNN for signal identification: a biological case study

Luca PinelloVito Di GesùGiosuè Lo Bosco

subject

Computer sciencebusiness.industryFeature vectorPattern recognitionmulti layer methodone class classifierPreprocessorSegmentationnucleosome positioning.Artificial intelligenceK nearest neighbourbusinessClassifier (UML)Multi layer

description

The paper describes an application of a one class KNN to identify different signal patterns embedded in a noise structured background. The problem becomes 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 preliminary signal segmentation in an interval feature space. The one class KNN has been tested on synthetic and real (Saccharomyces cerevisiae) microarray data in the specific problem of DNA nucleosome and linker regions identification. Results have shown, in both cases, a good recognition rate.

yearjournalcountryeditionlanguage
2009-01-01
10.1504/ijkesdp.2009.028989http://hdl.handle.net/10447/40105