Classification Using LSTM
Results
Model hyperparameters
Hyper-parameters |
Value |
Gradient Decay
Factor |
0.9000 |
Squared
Gradient Decay Factor |
0.9990 |
Epsilon |
1.0000e-08 |
Initial Learn
Rate |
0.0020 |
Learn Rate
Schedule |
'none' |
Learn Rate Drop
Factor |
0.1000 |
Learn Rate Drop
Period |
10 |
Hidden Layers |
45 |
L2Regularization |
1.0000e-04 |
Gradient
Threshold Method |
'l2norm' |
Max Epochs |
800 |
Mini Batch size |
1800 |
Sequence Length |
'shortest' |
Sequence Padding
Value |
0 |
Sequence
Padding Direction |
'right' |
Batch
Normalization Statistics |
'population' |
Confusion Matrix for WST features
the confusion chart shown above represents the test set accuracy of 91% using the LSTM approach following the above data division. On the sides (RHS and Bottom) the total percentages with respect to True Class and Predicted Class is given. It was seen that: 9 classes had classification accuracy of 100%, 2 classes had classification accuracy between 90-99%, 1 class had classification accuracy between 80-89%. The remaining 4 classes had lower classification due to less data being available
Receiver Operating Characteristics
The plot above shows the area under the curve of the 17 classes of arrhythmia. It shows true positive rate versus the false positive rate. The AUC is the multiple of the two factors
Comments
Post a Comment