Skip to main content

Table 4 Derived accuracies by learning algorithms with default parameters set by WEKA are listed above. Column 1 lists different algorithms

From: A model to predict the function of hypothetical proteins through a nine-point classification scoring schema

Algorithms

ALL

Cfs

PCA

Earlier study [25]

Current study

Earlier study [25]

Current study

Earlier study [25]

Current study

Selected Features â–¡

1,2,3,4,5,6

1,2,3,4,5,6,7,8,9

1 2 5 6

1,2,3,6,7,9

1,2,3,4,5,6

1,2,3,4,5,6,7,8

bayes_NaiveBayesUpdateable

55.21

96.67

54.05

96.67

72.97

93.00

functions_smo_npolyk

59.46

96.67

54.05

96.00

51.35

97.00

rules_DecisionTable

48.65

96.00

54.05

96.00

70.27

92.33

functions_mlp

81.08

97.67

59.46

96.67

81.08

96.00

bayes_nbay

54.05

96.67

54.05

96.67

72.97

93.00

trees_j48

67.57

97.00

51.35

96.00

72.97

97.00

Average

 

97.39

 

96.26

 

94.53

  1. Column 2 shows accuracies on the entire data through ten-fold cross-validation. Columns 3 and 4 show accuracies by different algorithms after applying feature selection algorithms as per the column header (Cfs Correlation Feature Selection, PCA Principal Component Analysis). Cfs uses best fit method and PCA uses Ranker method as set by WEKA