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FIX BorddelineSMOTE-2 use the full dataset to generate new sample#1023

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glemaitre merged 5 commits intoscikit-learn-contrib:masterfromglemaitre:is/846
Jul 10, 2023

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@glemaitreglemaitre commentedJul 10, 2023
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closes#861

Make sure that we use the full dataset to generate new samples inBorderlineSMOTE version 2.

@glemaitreglemaitre marked this pull request as draftJuly 10, 2023 19:24
@glemaitreglemaitre marked this pull request as ready for reviewJuly 10, 2023 20:39
@glemaitreglemaitre merged commit2859cb0 intoscikit-learn-contrib:masterJul 10, 2023

self.nn_k_.fit(X_to_sample_from)
nns = self.nn_k_.kneighbors(X_danger, return_distance=False)[:, 1:]
X_new, y_new = self._make_samples(
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This implementation does not fully reflect the description of Borderline smote 2 in the paper. The paper says that to create the samples by interpolation between the template of the minority and a neigbhour of the majority, it multiplies by a factor between 0 and 0.5 (instead of 0-1) to ensure the synthetic data is closer to the minority.

If I understand this code correctly, we are multiplying everything by a factor between 0 and 1. Pls correct me if I am wrong.

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Nop, indeed. I forgot to look at the next page of the article. I will try to propose a fix.

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Borderline SMOTE-2: how does itpick neighbours from the majority class using a KNN trained only on the minorty?
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