Imbalanced Data Classification Techniques

Commonly used software tools produce conflicting and overly-optimistic AUPRC values

The paper evaluates 10 software tools for computing AUPRC and finds they produce conflicting and overly-optimistic results.

The Use of Class Imbalanced Learning Methods on ULSAM Data to Predict the Case-Control Status in Genome-Wide Association Studies

This paper compares ML methods for predicting T2D status from GWAS data using imbalanced learning techniques, finding that MLP with SMOTE achieves the highest accuracy.

Class imbalance should not throw you off balance: Choosing the right classifiers and performance metrics for brain decoding with imbalanced data

This paper evaluates the impact of class imbalance on ML performance in neuroscience, showing that Accuracy is misleading and recommending Balanced Accuracy and Random Forest with stratified cross-validation.

MLcps: Machine Learning Cumulative Performance Score for classification problems

The paper introduces MLcps, a Python package that combines multiple performance metrics into a single score for classification problems, tested on biological datasets to assess model robustness.

METAbolomics data Balancing with Over-sampling Algorithms (META-BOA): an online resource for addressing class imbalance

META-BOA provides a web tool to balance metabolomics data using over-sampling algorithms and evaluates their impact through visualization and classification.