Classiﬁcation of highly unbalanced data using deep learning techniques

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Presentation transcript:

Classiﬁcation of highly unbalanced data using deep learning techniques Dianjing Liu

Thyroid Disease Data Set (Sick) (Not Sick) Negative: 3178 Positive: 250 Predicting all instances to be negative: 92.71% accuracy, 0% sensitivity. Testing accuracy of previous classifiers: Nearest-neighbor: 92.44% k-Nearest Neighbor: 93.70% Ref. [1]: 93.23% Bayes rule: 96.1% Nonlinear Bayes: 97.2% Ref. [2]: 97.23% Ref. [3]: 99.36%

Neural network settings Activation: sigmoid Training steps: 50,000 Learning rate: 0.002. Decays by half every 5,000 steps. Network size is chosen by cross validation:

Oversampling Original Oversampled Table 1. The accuracy and sensitivity of neural networks trained with different positive instance ratios.

Oversampling Original Oversampled ROC curve PR curve

Other techniques Activation function Dropout Batch normalization Adam Optimization algorithm

Other techniques Learning curves: Tabel 2. Testing results of different techniques (with or without oversampling). The ReLU activation and dropout methods fail to train effective models and predict all samples to be negative. Learning curves: Oversampling BN and Adam

Conclusion Oversampling can always improve sensitivity. BN and Adam have very good performance: higher accuracy, sensitivity and training speed. Best model: 98.60% accuracy. 99.60% sensitivity.