How to Tackle Imbalanced Datasets with Sampling Techniques

Introduction

Sampling is the process of generating sample points according to a specific probability distribution. While many programming languages provide direct sampling functions for simple distributions such as uniform or Gaussian, the underlying sampling process often requires careful design. For complex distributions without built-in samplers, more sophisticated methods are needed, making a deep understanding of sampling essential.

Scenario Description

When training a binary classification model, practitioners frequently encounter severe class imbalance—for example in medical diagnosis, network intrusion detection, or credit‑card fraud detection. If the positive‑negative ratio is 1:99, a naïve classifier that always predicts the majority class would achieve 99% accuracy, which is misleading because we desire good performance on both classes.

Problem

How should we process highly imbalanced training data to train a more effective classifier?

Answer and Analysis

Many models struggle with imbalanced data because the objective optimized during training (e.g., overall accuracy on a skewed training set) does not align with the evaluation criteria we care about (e.g., balanced accuracy or recall on each class). This mismatch can also arise from differing class weights between training and testing.

Two broad strategies can address imbalance:

1) Data‑based methods

These methods modify the training set to make it more balanced.

Random oversampling duplicates minority‑class samples, increasing data size and risk of over‑fitting. Random undersampling discards majority‑class samples, potentially losing useful information. The SMOTE algorithm synthesizes new minority samples, but can increase class overlap. Improved variants such as Borderline‑SMOTE (focus on samples near the decision boundary) and ADASYN (generate more samples for harder minority instances) mitigate these issues. Data‑cleaning techniques like Tomek Links can further reduce overlap.

Other practical approaches include cluster‑based sampling (using clustering information to guide oversampling/undersampling), data augmentation (adding noise, cropping, flipping, rotating images, etc.), and Hard Negative Mining (selecting difficult majority samples for training).

2) Algorithm‑based methods

When data is highly imbalanced, we can modify the learning algorithm itself—e.g., cost‑sensitive learning that assigns higher weight to minority‑class errors, or reformulating the problem as one‑class learning/anomaly detection. These topics are beyond the scope of this article but will be covered in future installments.

Extension and Summary

In interviews, this topic can be expanded to discuss evaluation metrics for imbalanced data (e.g., precision‑recall curves, F1‑score), how the choice of method varies with different imbalance ratios (e.g., 1:100 vs. 1:1000), and the relationship between cost‑sensitive learning and sampling techniques.

References

[1] H. He and E. A. Garcia, "Learning from Imbalanced Data," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263‑1284, Sep. 2009.