Table 6.
Results obtained with different kind of supervised neural networks applied to the 17 variables selected with TWIST system.
| ANN | train - test order | Sensitivity | Specificity | Overall accuracy |
|---|---|---|---|---|
| SelfSABp 3 | ab | 93.3% | 84.7% | 89.0% |
| TasmSABp 4 | ab | 93.3% | 83.2% | 88.3% |
| SelfDABp 1 | ab | 93.3% | 81.9% | 87.6% |
| SelfSABp 4 | ab | 86.7% | 88.2% | 87.5% |
| FF_Bp 5 | ab | 93.3% | 81.5% | 87.4% |
| SelfSABp 2 | ba | 93.3% | 81.2% | 87.3% |
| TasmDABp 1 | ba | 88.9% | 83.2% | 86.0% |
| SelfSABp 1 | ba | 86.7% | 83.2% | 84.9% |
| FF_Bp 3 | ba | 81.5% | 80.9% | 81.2% |
| SelfSABp 1 | ba | 81.5% | 80.9% | 81.2% |
| Average | 89.2% | 82.9% | 86.0% |
Notes: In the column under ANN are listed different kind of Artificial Neural Networks developed by Semeion Research Centre:
SelfSABp 3: self-recurrent-static-adaptive No.3.
TasmSABp 4: Temporal Associative Subjective Memory back propagation No.4.
SelfDABp 1: self-recurrent dynamic-adaptive No.1.
SelfSABp 3: self-recurrent-static-adaptive No.4.
FF_Bp 5: Feed forward Back propagation No.5.
SelfSABp 2: self-recurrent-static-adaptive No.2.
TasmSABp 4: Temporal Associative Subjective Memory dynamic adaptive No.1.
SelfDABp 1: self-recurrent dynamic-adaptive No.1.
SelfSABp 3: self-recurrent-static-adaptive No.4.
FF_Bp 5: Feed forward Back propagation No.5.
In column train-test order ab = train on subset a and test on subset b; ba = train on subset b and test on subset a.