Current research in downsampling also revolve around combining it with other techniques to create hybrid techniques. One combination is to both downsample and upsample the data to get the benefits of both: SMOTE+Tomek Link, Agglomerative Hierarchical Clustering (AHC), and SPIDER are a few examples...
Why reprex? Getting unstuck is hard. Your first step here is usually to create a reprex, or reproducible example. The goal of a reprex is to package your code, and information about your problem so that others can run it…
SMOTE The Synthetic Minority Oversampling Technique, or SMOTE, is an upsampling technique first proposed in 2002 that synthesizes new data points from the existing points in the minority class.4It consists of the following process:2 Find the K nearest neighbors for all minority class data points...
On contrary if i use SMOTE it's working fine on the same data. I've 31 GB RAM and data shape is (98000,48), its around 6.5 MB on disk. I am using python 3.5 and imblearn version is '0.4.2'. can somebody suggest some hack to deal with issue. Thanks. lisiqi commented Nov 13...
However, all that glitters is not gold: you just caused data leakage. In the code above, the transformation was applied before running cross-validation, which splits train and test sets on different folds. This is a very common scenario that can trick beginners into thinking that SMOTE inc...
The augmentation techniques used in deep learning applications depends on the type of the data. To augment plain numerical data, techniques such as SMOTE or SMOTE NC are popular. These techniques are generally used to address the class imbalance problem in classification tasks. ...
To balance the amount of training samples in two classes (high quality answers and normal answers), we applied the Smote algorithm to oversampling a minor part of the sample set. Classification results are evaluated based on precision, accuracy, and F1 value, and the results are compared with...