Hi Thanks a lot for your time and consideration. I have seen imblearn but my question is not related to it.
Best regards, On Tue, Jun 19, 2018 at 9:04 PM, Christos Aridas <ichk...@gmail.com> wrote: > Hi, > > Have you seen http://imbalanced-learn.org? > > Best, > Chris > > On Tue, 19 Jun 2018 17:53 S Hamidizade, <hamidizad...@gmail.com> wrote: > >> Hi >> >> I would appreciate if you could let me know what is the best way to >> categorize the approaches which have been developed to deal with imbalance >> class problem? >> >> *This article >> <https://www.sciencedirect.com/science/article/pii/S0020025513005124> >> categorizes them into:* >> >> 1. Preprocessing: includes oversampling, undersampling and hybrid >> methods, >> 2. Cost-sensitive learning: includes direct methods and meta-learning >> which the latter further divides into thresholding and sampling, >> 3. Ensemble techniques: includes cost-sensitive ensembles and data >> preprocessing in conjunction with ensemble learning. >> >> *The second <https://dl.acm.org/citation.cfm?id=2907070> classification:* >> >> 1. Data Pre-processing: includes distribution change and weighting >> the data space. One-class learning is considered as distribution change. >> 2. Special-purpose Learning Methods >> 3. Prediction Post-processing: includes threshold method and >> cost-sensitive post-processing >> 4. Hybrid Methods: >> >> *The third article >> <https://link.springer.com/article/10.1007/s13748-016-0094-0>:* >> >> 1. Data-level methods >> 2. Algorithm-level methods >> 3. Hybrid methods >> >> The last classification also considers output adjustment as an >> independent approach. >> >> Could you please let me know the class-weight in the sklearn's >> classifiers e.g., logistic regression is classified into which category? Is >> it true to say: >> >> In case of the first categorization, it falls into cost-sensitive learning >> >> In case of the second taxonomy, it would be classified into the third >> category i.e., cost-sensitive post-processing >> >> In case of the third classification, it should fall into algorithm level >> >> Best regards, >> _______________________________________________ >> 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 > >
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