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Table 8 Overall performance of various feature classifiers in 5-fold cross-validation for the classification of diverse plastid-types* (phase-II)

From: Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning

Feature type Sensitivity
(%)
Specificity
(%)
Accuracy
(%)
MCC Precision (%) ER (%) SVM kernel type
AAC 60.03 76.05 77.45 0.40 59.00 22.55 RBF (γ = 246, C = 1, j = 2)
PseAA 60.72 77.13 78.01 0.41 59.55 21.99 RBF (γ = 225, C = 1, j = 2)
Dipep 62.26 75.85 78.60 0.44 62.62 21.40 RBF (γ = 210, C = 1, j = 2)
NCC 60.97 77.34 78.39 0.42 58.51 21.61 RBF (γ = 5, C = 2, j = 3)
Phys-Chem 56.70 78.01 76.56 0.36 54.15 23.44 RBF (γ = 37, C = 9, j = 1)
  1. *classification of 4 plastid types: chloroplast, chromoplast, etioplast, amyloplast; individual performance of these classifiers on each class can be seen in supplementary material; AAC = amino acid composition, PseAA = Pseudo amino acid composition, Dipep = Dipeptide composition, NCC = Nterminal-Center-Cterminal composition (sequence divided into 3 parts), Phys-Chem = Protein physicochemical properties, MCC = Matthews Correlation Coefficient, ER = Error Rate, RBF = Radial Basis Function of SVM.