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Table 6 GrpClassifierEC: EC classifier results with a k value of 30 compared to Random forest applied on the EC samples and results for regular classifiers applied on the original data

From: GrpClassifierEC: a novel classification approach based on the ensemble clustering space

Data/performanceData infoEC classifier
GrpClassifierEC
Accuracy difference
#Sample#EC_SamplesratioSensitivitySpecificityF-measureAccuracyEC-RFRFDTTKNN
Aves vs Embryophyta106851348%0.860.940.850.92-0.01-0.030.02-0.01
Cercopithecidae vs Malvaceae89444950%0.940.920.940.940.040.010.060.03
Embryophyta vs Laurasiatheria95349352%0.940.830.940.910.040.000.060.03
Fabaceae vs Nematoda264253620%0.780.880.790.84-0.01-0.050.01-0.04
Hexapoda vs Aves2840164758%0.760.920.780.880.05-0.010.070.06
Laurasiatheria vs Brassicaceae120940634%0.890.880.890.880.00-0.040.00-0.03
Malvaceae vs Fabaceae140145132%0.550.800.530.730.07-0.040.060.03
brassicaceae vs Hexapoda258454221%0.770.950.780.91-0.01-0.030.01-0.02
Hominidae vs Cercopithecidae182978643%0.610.870.630.800.100.040.140.09
Monocotyledons vs HomoSapiens262585533%0.860.870.860.870.04-0.030.03-0.01
Average  39%80%89%80%87%3%-2%5%1%
  1. K is number of clusters. The section “Accuracy Difference” is EC Classifier-ACC of the other classifier. A positive value indicates that the EC classifier is better than the other corresponding classifiers. EC-RF is a random forest applied on the EC data, RF is a random forest applied on the original data. DTT is a decisionTrees while KNN is K- Nearest Neighbors applied on the original data