SubSample would remove a lot of information from the negative class. I have more than 500 samples of negative class and just 5 samples of positive class.
Amita On Thu, Aug 4, 2016 at 4:43 PM, Nicolas Goix <goix.nico...@gmail.com> wrote: > Hi, > > Yes you can use your labeled data (you will need to sub-sample your normal > class to have similar proportion normal-abnormal) to learn your > hyper-parameters through CV. > > You can also try to use supervised classification algorithms on `not too > highly unbalanced' sub-samples. > > Nicolas > > On Thu, Aug 4, 2016 at 5:17 PM, Amita Misra <amis...@ucsc.edu> wrote: > >> 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 >> >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > -- Amita Misra Graduate Student Researcher Natural Language and Dialogue Systems Lab Baskin School of Engineering University of California Santa Cruz
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