Saturday 4:00 PM–4:45 PM in Theater
Resampling techniques and other strategies for handling highly unbalanced datasets in classification
Ajinkya More
- Audience level:
- Intermediate
Description
Many real world machine learning problems need to deal with imbalanced class distribution i.e. when one class has significantly higher/lower representation than the other classes. Often performance on the minority class is more crucial, e.g. fraud detection, product classification, medical diagnosis, etc. In this talk I will discuss several techniques to handle class imbalance in classification.
Abstract
I will discuss techniques to combat class imbalance in classification problems. These mainly fall into the following categories:
- Asymmetric loss functions: Penalize misclassifications on the class of interest higher (often the minority class)
- Resampling techniques:
- Undersampling the minority class
- Oversampling the majority class
- SMOTE (Synthetic minority oversampling technique)
- Tomek link removal
- Condensed nearest neighbors
- Edited nearest neighbors
- Ensemble techniques
- EasyEnsemble
- BalanceCascade
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