Expression-based classification of tumors requires stable, reliable and variance reduction methods, as DNA microarray data are characterized by low size, high dimensionality, noise and large biological variability. In order to address the variance and curse of dimensionality problems arising from this difficult task, we propose to apply bagged ensembles of Support Vector Machines (SVM) and feature selection algorithms to the recognition of malignant tissues. Presented results show that bagged ensembles of SVMs are more reliable and achieve equal or better classification accuracy with respect to single SVMs, whereas feature selection methods can further enhance classification accuracy.
|Titolo:||Cancer recognition with bagged ensembles of Support Vector Machines|
VALENTINI, GIORGIO (Primo)
|Parole Chiave:||Bagging; DNA microarray; Molecular classification of tumors; Support vector machines|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||gen-2004|
|Digital Object Identifier (DOI):||10.1016/j.neucom.2003.09.001|
|Appare nelle tipologie:||01 - Articolo su periodico|