Computational Intelligence and Neuroscience / 2017 / Article / Tab 2 / Research Article
A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data Table 2 Average AUC and corresponding standard deviation of the different methods. The first class listed in the bracket indicates the normal class. Results were calculated from 50 iterations.
SVDD OCSVM aK-LPE OCSVM-FA Our model OAR (Stand versus others) AUC 0.94 ±0.01 0.95 ±0.01 0.96 ±0.00 0.98 ±0.02 0.99 ±0.00OAR (Sit versus others) AUC 0.88 ±0.01 0.87 ±0.02 0.90 ±0.04 0.90 ±0.01 0.95 ±0.01OAR (Lie versus others) AUC 0.95 ±0.01 0.95 ±0.01 0.97 ±0.00 0.98 ±0.01 0.99 ±0.00GAS (Ethanol versus others) AUC 0.92 ±0.02 0.92 ±0.02 0.94 ±0.01 0.93 ±0.02 0.97 ±0.01GAS (Ethylene versus others) AUC 0.91 ±0.04 0.91 ±0.04 0.92 ±0.03 0.94 ±0.02 0.98 ±0.01GAS (Ammonia versus others) AUC 0.92 ±0.02 0.93 ±0.02 0.95 ±0.01 0.96 ±0.01 0.99 ±0.00GAS (Acetone versus others) AUC 0.81 ±0.03 0.77 ±0.04 0.80 ±0.03 0.80 ±0.03 0.92 ±0.01MPID (background versus signal) AUC 0.70 ±0.04 0.77 ±0.03 0.75 ±0.03 0.79 ±0.03 0.82 ±0.01MPID (signal versus background) AUC 0.66 ±0.09 0.72 ±0.11 0.73 ±0.03 0.70 ±0.03 0.76 ±0.01KDD 2008 (benign versus malignant) AUC 0.50 ±0.03 0.51 ±0.03 0.34 ±0.01 0.50 ±0.01 0.52 ±0.01Rank 4.3 3.90 3.10 2.65 1.05