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 >
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