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Figure 4 | BMC Bioinformatics

Figure 4

From: Boosting accuracy of automated classification of fluorescence microscope images for location proteomics

Figure 4

Dependence of classifier performance on number of input features. The average performances of neural network (filled circle), SVM (open diamond), AdaBoost (filled triangle), Bagging (filled square), Mixtures-of-Experts (filled diamond), and majority-voting ensemble (open square) classifiers are shown as a function of the number of features used to train the classifier. Average performance is defined as the average fraction of images in ten (2D) or eleven (3D) classes that were correctly classified over ten cross-validation trials. The features in SLF7DNA (A) or SLF9 (B) were ranked in order of their ability to discriminate the classes using SDA and increasing numbers of the features were used to train classifiers.

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