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Fig. 4 Accuracy result for classification with a NB classifier and a SVM classifer.
Fig. 7 Receiver operating curve for classification with CNN and classification based on areas.
For classification with multiple classes, the majority class prediction or a inconclusive value could be used.
With nine features, feed-forward neural network is used for classification with accuracy of 97%.
The preliminary analysis suggests that the features can be used for classification with Gaussian Mixture Models (GMM).
In the experiments with FA for classification with oracle segmentation, we assess different configurations for the number of x factors and the scoring methods described previously.
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For classifications with texture information, the best accuracies were found in both images for mean and angular second moment texture parameters.
Pathway based similarity measuring method, PEPC, consistently shows higher classification accuracies than CGSEP method for classifications with three different search databases.
Support Vector Machines [ 31] was used for classification of samples between two categories, and Diagonal Linear Discriminant Analysis, which avoids complex models with excessive parameters in order to avoid over fitting data without loss of performance [ 32] was used for classifications with more than two categories.
The mean accuracy for classification tasks with IR ≤ 0.25 was 81%, while for tasks with IR > 0.25 the mean accuracy was 70%.
That means that the impact of the different AD measures depends on the level of difficulty of the classification problem (expressed as AUC ROC) and will be largest for classification problems with intermediate difficulty (range AUC ROC: 0.7 0.9).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com