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.
L. Nanni, CN Hsu, A. Lumini, YS Lin, CC Lin (2009). Automated cell phenotype image classification combining different methods. s.l : s.n.
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.
