Classification
Neural and Non-neural Techniques for Classification
|
Classifier |
Classification
Accuracy (%) Test Set |
Comments |
|
Shallow NN |
99.9% |
Number of neurons:
12, Architecture:
5In|5out, ,Preprocessor: PCA |
|
LSTM |
98.9% |
Number of neurons:
33, Architecture:
5In|5out,
Preprocessor: PCA, Activation:
Softmax |
|
Fine Tree |
98.8% |
Max. Number of Splits = 100,
Split Criterion: Gini’s Diversity index |
|
Medium Tree |
98.7% |
Max. Number of Splits = 20,
Split Criterion: Gini’s Diversity index |
|
Coarse Tree |
60.1% |
Max. Number of Splits = 4,
Split Criterion: Gini’s Diversity index |
|
Linear
Discriminant |
40.9% |
Full Covariance Structure |
|
Quadratic
Discriminant |
63.6% |
Full Covariance Structure |
|
Linear SVM |
58.7% |
Kernel Function: Linear |
|
Cubic SVM |
94.7% |
Kernel Function: Cubic |
|
Quadratic
SVM |
83.6% |
Kernel Function: Quadratic |
|
Ensemble (Rus
Boosted Trees) |
98.7% |
Ensemble Method: RUSBoost,
Learner: Decision Tree, Max. Splits: 20, Number of Learners: 30, LR: 0.1 |
|
Ensemble (Bagged
Trees) |
98.8% |
Ensemble Method: BagBoost,
Learner: Decision Tree, Max. Splits: 1049, Number of
Learners: 30 |
|
Ensemble (Subspace
Discriminant) |
39.6% |
Ensemble Method: SubspaceBoost,
Learner: Discriminant, Max. Splits: 20, Number of Learners: 30, LR: 0.1 |
|
Ensemble (Subspace
KNN) |
98.9% |
Ensemble Method: SubspaceKNN,
Learner: Discriminant, Max. Splits: 20, Number of Learners: 30, LR: 0.1 |
Confusion Matrix
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