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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 □ 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