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We conducted a comprehensive study of estimation accuracy along three dimensions as introduced in Affect analysis framework: (1) different classifiers of SVM and k NN, (2) different combinations of affective sources (acoustic: A, lexical: L and discouse: D), and (3) different feature selection schemes (Base, f10, f15, and PCA), which are shown in Figure 4.
These schemes base upon either a fixed-bit allocation principle (assigning a fixed number of bits to each feature dimension) [4 7, 10, 13, 16, 20] or a dynamic-bit allocation principle (assigning a different number of bits to each feature dimension) [1, 3, 17 19, 21].
Figure 4 Classification accuracy for different combinations of affective sources (A = Acoustic, L = Lexical, D = Discourse), feature selection schemes ( Base, f10, f15, PCA ) and different classification techniques of (a) xtitSVM, (b) SVM-5WC, (c) SVM-10WC, (d) SVM - 5CV, (e) kNN, and (f) k NN- 5CV.
The analysis was run under 5 substitution schemes, base frequencies estimation (+F), gamma shape (+G) and invariable sites (+I) estimation, which makes a total of 40 models.
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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