In this paper our aim is to study how an ensemble of classifiers can improve the performance of a machine learning technique for cell phenotype image classification. We want to point out some of the advantages that an ensemble of classifiers permits to obtain respect a stand-alone method. Finally, the preliminary results on the 2D-HeLa dataset, obtained by the fusion between a random subspace of Levenberg-Marquardt neural networks and a variant of the AdaBoost, are reported. It is interesting to note that the proposed system obtains an outstanding 97.5% Rank-1 accuracy and a >99% Rank-2 accuracy.
Automated cell phenotype image classification combining different methods / L. Nanni; CN Hsu; A. Lumini; YS Lin; CC Lin. - ELETTRONICO. - (2009), pp. 1-4. (Intervento presentato al convegno Workshop on Automated Interpretation and Modeling of Cell Images (ICML-UAI-COLT2009) tenutosi a Montreal, Canada nel June 2009).
Automated cell phenotype image classification combining different methods
NANNI, LORIS;LUMINI, ALESSANDRA;
2009
Abstract
In this paper our aim is to study how an ensemble of classifiers can improve the performance of a machine learning technique for cell phenotype image classification. We want to point out some of the advantages that an ensemble of classifiers permits to obtain respect a stand-alone method. Finally, the preliminary results on the 2D-HeLa dataset, obtained by the fusion between a random subspace of Levenberg-Marquardt neural networks and a variant of the AdaBoost, are reported. It is interesting to note that the proposed system obtains an outstanding 97.5% Rank-1 accuracy and a >99% Rank-2 accuracy.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
