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. 2016 Sep 21;2016:2401496. doi: 10.1155/2016/2401496

Table 8.

Comparison results and error analysis on NASA datasets.

NASA datasets Techniques Sensitivity Specificity FPR or pf Balance Accuracy AUC MSE (error)
CM1 Naïve Bayes [5] 71.03 78.65 34.09 68.37 64.57 0.75 0.1456
Random Forest [5] 70.09 71.29 32.17 68.94 60.98 0.74 0.2314
C4.5 Miner [5] 74.91 74.66 27.68 73.58 66.71 0.53 0.3765
Immunos [5] 73.65 75.02 30.99 71.24 66.03 0.63 0.1732
ANN-ABC [5] 75.00 81.00 33.00 71.00 68.00 0.77 0.2435
Hybrid self-organizing map [13] 70.12 78.96 30.65 69.73 72.37 0.80 0.0810
Support vector machine [14] 78.97 79.08 31.27 73.35 78.69 0.79 0.0154
Majority Vote [14] 79.80 80.00 30.46 74.16 77.01 0.81 0.1968
AntMiner+ [14] 80.65 78.88 30.90 74.22 79.43 0.84 0.0345
Proposed ADBBO-RBFNN model 81.92 80.96 29.71 75.41 82.57 0.90 0.0067

JM1 Naïve Bayes [5] 68.98 69.77 36.54 66.11 60.78 0.68 0.6547
Random Forest [5] 66.72 72.38 33.47 66.62 63.97 0.75 0.6721
C4.5 Miner [5] 69.08 68.55 40.67 63.87 62.35 0.61 0.5498
Immunos [5] 70.99 70.21 43.00 63.32 64.55 0.63 0.4219
ANN-ABC [5] 71.00 73.05 41.00 64.00 61.00 0.71 0.4057
Hybrid self-organizing map [13] 71.02 74.90 40.57 64.75 72.33 0.82 0.5692
Support vector machine [14] 70.89 79.00 39.87 65.09 70.32 0.81 0.3759
Majority Vote [14] 74.65 73.46 40.36 66.30 75.92 0.83 0.0345
AntMiner+ [14] 75.81 80.96 37.12 68.67 74.51 0.72 0.1786
Proposed ADBBO-RBFNN model 79.85 82.31 36.22 70.69 77.03 0.87 0.0156

KC1 Naïve Bayes [5] 74.33 76.85 35.71 68.90 65.87 0.79 0.9854
Random Forest [5] 72.54 75.89 37.91 66.90 67.99 0.80 0.6231
C4.5 Miner [5] 76.42 75.64 34.05 70.71 68.01 0.64 0.7893
Immunos [5] 78.05 72.91 36.92 69.63 63.55 0.71 0.6451
ANN-ABC [5] 79.00 77.00 33.00 72.00 69.00 0.80 0.2257
Hybrid self-organizing map [13] 80.92 80.94 35.67 71.40 78.43 0.86 0.1847
Support vector machine [14] 81.37 81.27 28.96 75.65 79.24 0.83 0.5467
Majority Vote [14] 82.65 85.62 30.98 74.89 79.66 0.85 0.0578
AntMiner+ [14] 84.29 84.99 26.11 80.40 80.51 0.90 0.0346
Proposed ADBBO-RBFNN model 85.67 87.95 20.24 82.46 84.96 0.92 0.0239

KC2 Naïve Bayes [5] 77.24 75.98 24.57 76.32 74.00 0.82 0.1453
Random Forest [5] 70.32 70.71 23.90 73.05 77.81 0.82 0.5498
C4.5 Miner [5] 69.87 74.67 29.19 70.34 76.54 0.67 0.6672
Immunos [5] 76.51 75.92 25.06 75.71 72.90 0.73 0.4591
ANN-ABC [5] 79.00 76.00 21.00 79.00 79.00 0.85 0.3195
Hybrid self-organizing map [13] 80.98 77.82 23.09 78.85 85.98 0.91 0.1666
Support vector machine [14] 84.35 78.96 25.61 78.78 87.12 0.88 0.2789
Majority Vote [14] 86.71 84.77 20.38 82.80 83.47 0.82 0.1087
AntMiner+ [14] 86.07 83.98 21.88 81.66 90.86 0.80 0.0985
Proposed ADBBO-RBFNN model 87.96 86.24 17.93 84.73 95.65 0.95 0.0067

PC1 Naïve Bayes [5] 87.98 82.34 42.31 68.90 60.00 0.70 0.7689
Random Forest [5] 82.31 80.99 46.71 64.68 63.98 0.85 0.6792
C4.5 Miner [5] 76.58 81.76 38.24 68.29 62.18 0.68 0.5564
Immunos [5] 81.99 79.66 39.00 69.62 61.73 0.64 0.4987
ANN-ABC [5] 89.00 83.00 37.00 73.00 65.00 0.82 0.3125
Hybrid self-organizing map [13] 86.79 85.67 35.60 73.15 95.87 0.87 0.1325
Support vector machine (SVM) [14] 80.98 86.59 34.98 71.85 92.45 0.76 0.2037
Majority Vote [14] 84.61 84.37 36.08 72.26 92.50 0.85 0.1078
AntMiner+ [14] 89.34 87.12 37.29 72.58 91.85 0.91 0.0987
Proposed ADBBO-RBFNN model 90.89 89.33 30.23 73.49 96.29 0.93 0.0379