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when use SOMO,Why did the two types of samples not reach a balance and the number did not change #39
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There can be multiple reasons for that. In many cases the authors of a particular SMOTE variant did not cover all the possible corner cases, for example,
all of these because of the nature of the data is not compatible with the parameter settings and presumptions of the SMOTE variant. Where I found reasonable resolutions, I implemented them, in those cases when it is unfeasible (for example, determining the 5 closest neighbors when you have only 3 samples in a class), the data is returned unaltered, although I would expect some message in the logs if logging is enabled. Most likely your data is a corner case of the SOMO implementation with the parameters you used. Adjusting the parameters might lead to a properly operating SOMO. Also, if you share a minimal working example, I can look into it. |
thanks for your reply, i wrote a code like this: pip install -U imbalanced-learn datasets = fetch_datasets(filter_data=['oil']) oversampler= sv.SOMO() [print('Class {} has {} instances after oversampling'.format(label, count)) and the print result : |
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