Hi, I am currently exploring the problem of speed bump detection using accelerometer time series data. I have extracted some features based on mean, std deviation etc within a time window.
Since the dataset is highly skewed ( I have just 5 positive samples for every > 300 samples) I was looking into One ClassSVM covariance.EllipticEnvelope sklearn.ensemble.IsolationForest but I am not sure how to use them. What I get from docs separate the positive examples and train using only negative examples clf.fit(X_train) and then predict the positive examples using clf.predict(X_test) I am not sure what is then the role of positive examples in my training dataset or how can I use them to improve my classifier so that I can predict better on new samples. Can we do something like Cross validation to learn the parameters as in normal binary SVM classification Thanks,? Amita Amita Misra Graduate Student Researcher Natural Language and Dialogue Systems Lab Baskin School of Engineering University of California Santa Cruz -- Amita Misra Graduate Student Researcher Natural Language and Dialogue Systems Lab Baskin School of Engineering University of California Santa Cruz
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