Tomek Links: Undersampling Technique for Fraud Detection

In fraud detection problems, one of the biggest challenges Data Scientists / ML Engineers face is dealing with imbalanced datasets. Since fraudulent transactions are typically rare compared to legitimate ones, making it difficult for models to learn the patterns of fraud effectively.

Tomek Links is an under sampling technique that can significantly improve your fraud detection models. In this post, let’s explore what Tomek Links are, how they work, and why they’re particularly useful in fraud detection.

What are Tomek Links?

Tomek Links, introduced by Ivan Tomek in 1976, are pairs of instances from different classes that are each other’s nearest neighbors. In the context of fraud detection, a Tomek Link would be a fraudulent transaction and a legitimate transaction that are very similar to each other.

How Tomek Links Work

1. Identifying all Tomek Links in the dataset
2. Removing the majority class instance (usually the legitimate transaction) from each pair
3. This process effectively “cleans” the overlap between classes

Implementing Tomek Links in Python

Let’s look at how to implement Tomek Links using the `imbalanced-learn` library in Python:

from imblearn.under_sampling import TomekLinks
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn.ensemble import RandomForestClassifier

# Create an imbalanced dataset
X, y = make_classification(n_samples=10000, n_classes=2, weights=[0.99, 0.01], 
n_features=20, random_state=42)

# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Apply Tomek Links
tl = TomekLinks()
X_resampled, y_resampled = tl.fit_resample(X_train, y_train)

# Train a model
clf = RandomForestClassifier(random_state=42)
clf.fit(X_resampled, y_resampled)

# Evaluate the model
y_pred = clf.predict(X_test)
print(classification_report(y_test, y_pred))

Use Cases in Fraud Detection

Tomek Links are particularly useful in fraud detection for several reasons:

1. Boundary Clarification: They help clarify the boundary between fraudulent and legitimate transactions by removing ambiguous cases.
2. Focus on Difficult Cases: By keeping the fraudulent transactions and removing only the similar legitimate ones, the algorithm focuses on the most challenging cases.
3. Noise Reduction: It can help reduce noise in the majority class, which is often present in real-world fraud detection datasets.
4. Improved Model Performance: By balancing the dataset and clarifying class boundaries, Tomek Links can lead to improved performance in subsequent classification models.

Gotchas and Considerations

While Tomek Links can be powerful, there are some things to keep in mind:

1. Data Loss: Removing instances can lead to loss of potentially important information. Use this technique judiciously.
2. Not a Standalone Solution: For highly imbalanced datasets (like many fraud detection scenarios), Tomek Links alone may not be sufficient. Consider combining it with other techniques.
3. Computational Complexity: For very large datasets, finding Tomek Links can be computationally expensive.
4. Sensitivity to Noise: In noisy datasets, Tomek Links might remove legitimate data points that are actually informative.

Conclusion

Tomek Links offer a sophisticated approach to handling imbalanced datasets in fraud detection. By focusing on the boundary cases and cleaning up the overlap between classes, this technique can significantly enhance the performance of your fraud detection models. However, like any data preprocessing technique, it should be used thoughtfully and in combination with domain knowledge and other balancing methods.

Remember, the key to effective fraud detection lies not just in advanced algorithms, but in a deep understanding of your data and the careful application of techniques like Tomek Links.