Ensemble Resampling Strategies for Imbalanced Data

In our previous article on advanced data sampling techniques for imbalanced datasets, we explored how to prepare your data for machine learning models. You learned about building data quality pipelines and handling missing values to ensure your data is ready for analysis. Now, let's tackle one of the most common challenges in classification tasks: imbalanced datasets.

Why Imbalanced Data Matters

When working with classification problems, you'll often encounter datasets where one class significantly outnumbers others. Think of fraud detection, where legitimate transactions might make up 99.9% of your data, or rare disease diagnosis, where positive cases are just 1-2% of patient records.

Standard machine learning algorithms struggle with imbalanced data because they optimize for overall accuracy. This leads to models that often predict the majority class and end up missing the vital minority cases that you actually care about.

Fig 1: Standard algorithms tend to create decision boundaries that favor the majority class, often misclassifying most of the minority class to minimize the overall error rate.

Class imbalance is particularly problematic when the minority class is what you're interested in detecting, like fraudulent transactions or rare diseases. The model achieves high accuracy by simply predicting everything as the majority class, but fails at its actual purpose.

Consider a dataset with 98% negative samples and 2% positive samples. A model that simply predicts "negative" for everything would achieve 98% accuracy while being completely useless for identifying the positive cases. You'll see the same in Fig. 1.

Four Powerful Ensemble Resampling Techniques

To address these challenges, researchers have developed sophisticated ensemble resampling techniques that combine the power of multiple models with intelligent sampling strategies. Let's explore four effective approaches that can significantly improve your models when working with imbalanced datasets.

1. EasyEnsemble

EasyEnsemble tackles class imbalance by combining undersampling with ensemble learning. It works by creating multiple balanced subsets of your data and training separate models on each subset.

Fig 2: EasyEnsemble Technique

How does it work?

Create multiple balanced subsets by randomly undersampling the majority class while keeping all minority samples.
Train a separate classifier on each balanced subset.
Combine the predictions from all classifiers through voting or averaging.

Let’s take an example to understand this better. In a credit card fraud detection with 10,000 legitimate transactions and 100 fraudulent ones, EasyEnsemble might create 10 balanced datasets, each containing all 100 fraud cases and 100 randomly selected legitimate transactions. Each classifier learns from a different subset of legitimate transactions, but all learn from the complete set of fraudulent patterns.

In Fig. 2, you’ll see how EasyEnsemble creates multiple balanced datasets by randomly undersampling the majority class while preserving all minority samples in each subset. The diverse majority samples help create multiple perspectives that are combined in the final ensemble.

When should you use it?

When you have a very large majority class
When computational efficiency is important
When you need improved recall without sacrificing too much precision

2. RUSBoost

RUSBoost combines Random Undersampling (RUS) with boosting techniques. It focuses on iteratively improving the model's performance on difficult examples, especially those from the minority class.

Fig 3: RUSBoost Technique

How does it work?

Assign initial equal weights to all training examples
In each boosting iteration:
- Create a balanced subset by randomly undersampling the majority class.
- Train a weak classifier on this balanced subset.
- Evaluate the classifier and increase weights for misclassified examples.
Combine all weak classifiers with their respective weights for the final model.

RUSBoost progressively focuses on harder cases through the boosting mechanism. If a minority class example is misclassified by early classifiers, it receives more attention in later iterations.

For instance, in disease diagnosis, RUSBoost might first learn common patterns that distinguish healthy patients from those who are sick. As training progresses, it gives more weight to borderline cases with subtle symptoms that were initially misclassified, helping the final model detect early-stage disease that might otherwise be missed.

See Fig. 3. RUSBoost randomly undersamples the majority class at every boosting round, then increases the weights of misclassified samples so subsequent learners focus on them. All minority samples are retained in each iteration, while the α-weights give more influence to better-performing classifiers. RUSBoost behaves like AdaBoost but with random undersampling each round.

When should you use it?

When you need better handling of difficult-to-classify examples
When other resampling methods produce too many false positives
When you need models that learn incrementally from mistakes

3. BalanceCascade

BalanceCascade is an adaptive ensemble approach that progressively refines the majority class samples. Unlike fixed undersampling methods, it intelligently removes majority samples that are already well-classified by previous stages.

Fig 4: BalanceCascade Technique

How does it really work?

Create a balanced subset by randomly undersampling the majority class.
Train a classifier on this balanced subset.
Use the classifier to identify correctly classified majority examples.
Remove those well-classified examples from the training data.
Repeat the process, creating a cascade of classifiers that focus on increasingly difficult examples.
Combine all classifiers for the final prediction.

As we saw in the previous two cases, BalanceCascade is particularly efficient in terms of computational resources. By progressively removing easy-to-classify examples, subsequent models can focus entirely on the more challenging cases that require additional learning.

In spam detection, the first classifier might learn to identify obvious spam based on common keywords. These emails are then removed from the training pool, allowing subsequent classifiers to focus on more sophisticated spam that uses evasion techniques and requires more nuanced detection.

In Fig. 4, you’ll notice how BalanceCascade progressively removes well-classified majority samples after each stage, focusing each new classifier on increasingly difficult examples. All minority samples are retained throughout the process, with each stage becoming more specialized in handling complex edge cases.

When should you use this technique?

When you have a very large and diverse majority class
When you want to focus on the "hard" cases that other methods might miss
When you need to maximize information usage from majority samples

4. Dynamic Undersampling Pipelines

Dynamic undersampling adapts its sampling strategy during training, based on what the model has already learned. This approach is particularly effective for complex, evolving datasets.

Fig. 5: Dynamic Undersampling Pipeline Method

How does it work?

Begin with a standard undersampling approach to create balanced training batches.
As training progresses, evaluate which majority class examples are frequently misclassified.
Adjust sampling probabilities to focus on "hard" examples that the model struggles with.
Reduce or eliminate sampling of "easy" examples that are consistently classified correctly.
Combine models from different stages for a final ensemble classifier.

Dynamic undersampling essentially learns which examples provide the most valuable information for the model. By preferentially sampling these examples, training becomes more efficient and the model learns more effectively. This’ll be evident in Fig. 5, i.e., dynamic undersampling adapts sampling probabilities during training, focusing more on challenging majority examples and less on easy ones as training progresses.

For example, in customer churn prediction, some loyal customers are clearly "non-churners," while others exhibit patterns similar to those of customers who eventually leave. Dynamic undersampling would focus on these ambiguous cases, helping the model learn the subtle differences between satisfied customers and those at risk of churning.

When should you use it?

When dealing with complex, heterogeneous majority classes
When computational resources are limited and you need efficient training
When your dataset contains many redundant or easily classified majority samples

How Do You Choose the Right Ensemble Strategy

Here’s a table (See Table 1) to help you decide the right ensemble strategy you can use.

Imbalanced Learning Strategies – Strengths, Weaknesses, and Best Uses
Strategy	Strengths	Weaknesses	Best For
EasyEnsemble	Computationally efficient Simple to implement Preserves all minority examples	May miss important majority patterns Less adaptive than other methods	Very large datasets High imbalance ratios When simplicity is valued
RUSBoost	Focuses on difficult examples Effective with noisy data Progressive improvement	Can overfit to minority class Higher computational cost Complex to tune properly	Noisy datasets When recall is critical Complex decision boundaries
BalanceCascade	Focuses progressively on harder cases Efficient use of majority examples Good for complex boundaries	Can lose information in early stages More complex implementation Sequential dependency limits parallelization	When majority class has varied patterns When more adaptive learning is needed Moderate to high imbalance
Dynamic Undersampling	Most adaptive to learning progress Excellent for complex datasets Efficient use of training time	Most complex implementation Requires monitoring infrastructure More hyperparameters to tune	Complex, heterogeneous data When training efficiency matters Problems with changing patterns

Table 1: The strengths and weaknesses of all ensemble techniques we’ve seen

Conclusion

Let’s do a quick recap of all the things we’ve learnt. When working with real-world data, you'll often face datasets where one class dramatically outnumbers others. Think fraud detection, where legitimate transactions make up 99.9% of your data, or medical diagnosis, where positive cases are just 1-2%.

This imbalance can be a real headache, as traditional models tend to predict the majority class and often overlook the rare cases that you actually care about. That's where these ensemble resampling techniques work best.

Each approach has its own personality:

EasyEnsemble is your straightforward friend that’s simple to implement and efficient when you're short on time
RUSBoost is the detail-oriented teammate who excels at catching those tricky edge cases
BalanceCascade works like a detective, progressively focusing on increasingly challenging examples
Dynamic Undersampling is the adaptive learner, adjusting its strategy based on what it figures out along the way

By incorporating these techniques into your toolkit, you'll notice a significant improvement in how effectively your models can identify those important but rare events. Your fraud detection system will catch more actual fraud, your medical diagnosis tool will identify more positive cases, and your customer churn prediction will flag at-risk customers before they leave.

That said, you don't need to completely overhaul your existing pipelines. These methods, along with SMOTE and ensemble learning for class imbalance, can integrate with your current workflow to enhance performance.