imbalanced-seismic-data-classification

data.rar: The data sets were downloaded from the data mining competion: AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines
seismic data preprocessing.ipynb
dara_pre.py
model.py
feature combined.ipynb
MLP.py: Multilayer Perceptron model
CNN_series.py: Convolutional Neural Network as the classifer.
FCN.py: Fully Convolutional Neural Network as the classifer.
res_net.py: Residual Network as the classifer.
LSTM_FCN.py： LSTM combined with FCN as the classfier.
classifier_train.py: train and test the classifer with the processed and filed data sets. For rebalance the training samples, SMOTE(Synthetic Minority Over-sampling Technique) was applied and 10-fold cross validation was used during the training period. Change the "NET", such as MLP, CNN_series or FCN, you can use different models to do the imbalanced data classification. The messures are confusion matrix, ROC AUC, G-mean, F1 score and so on.
classify_train_without_features.py: Same with classifier_train.py, but the input data samples without features.

Provide feedback

Name		Name	Last commit message	Last commit date
Latest commit History 51 Commits
result_analysis		result_analysis
CNN_series.py		CNN_series.py
FCN.py		FCN.py
LSTM_FCN.py		LSTM_FCN.py
MLP.py		MLP.py
README.md		README.md
classify_train.py		classify_train.py
classify_train_without_features.py		classify_train_without_features.py
data.rar		data.rar
data_pre.py		data_pre.py
feature combined.ipynb		feature combined.ipynb
model.py		model.py
res_net.py		res_net.py
seismic data preprocessing.ipynb		seismic data preprocessing.ipynb