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Fraud Detection & Anomaly Detection • Handling Imbalanced Data (SMOTE, Class Weighting, Focal Loss)Medium⏱️ ~2 min
SMOTE: Synthetic Minority Oversampling Technique
Synthetic Minority Oversampling Technique (SMOTE) addresses imbalance by generating synthetic minority class examples rather than simply duplicating existing ones. The algorithm works in feature space: for each minority example, it finds its k nearest minority neighbors (typically k equals 5), then creates new synthetic points by interpolating along the line segments connecting the example to its neighbors. If a fraud transaction has features like transaction amount of 500 dollars and velocity of 10 transactions per hour, and its neighbor has 800 dollars and 15 transactions per hour, SMOTE might generate a synthetic example at 650 dollars and 12.5 transactions per hour.
This interpolation expands the minority class region and can produce smoother, more generalizable decision boundaries compared to naive oversampling that just duplicates points. When you duplicate the same fraud example 50 times, the model overfits to that exact feature combination. SMOTE generates variations that help the model learn the broader pattern. The technique is particularly effective when minority examples are sparse in feature space and you need to fill gaps between isolated positive clusters.
The trade-offs are significant. SMOTE increases training data size, which raises computational cost and memory requirements. If your original dataset has 1 million transactions with 2,000 fraud cases, expanding fraud to 20,000 synthetic examples adds 18,000 rows and can extend training time by 1.5 to 3 times. More critically, SMOTE can create synthetic points that cross class boundaries. In high dimensional or sparse feature spaces like text with 10,000 dimension bag of words vectors, linear interpolation produces dense vectors that do not resemble real documents. This manifold intrusion creates label noise and causes precision to collapse.
SMOTE works best with tabular data that has continuous features and clear metric structure. It struggles with categorical features (interpolating between category codes is meaningless), high dimensional sparse data, and extremely non-linear boundaries. Teams at payment processors use SMOTE cautiously, often restricting synthesis to borderline minority examples near the decision boundary rather than all positives. For text or image problems, class weighting is typically preferred over SMOTE because feature geometry does not support meaningful interpolation.
💡 Key Takeaways
•SMOTE generates synthetic minority examples by interpolating between each minority point and its k nearest minority neighbors in feature space (typically k equals 5)
•Expands minority class regions to create smoother decision boundaries compared to naive duplication, helping models generalize beyond exact training examples
•Training cost increases significantly: expanding 2,000 fraud cases to 20,000 synthetic examples can extend training time by 1.5 to 3 times
•Manifold intrusion risk: in high dimensional sparse spaces like text with 10,000 dimensions, interpolation creates dense synthetic points that do not resemble real data
•Works best with tabular data and continuous features; struggles with categorical variables, text, and images where interpolation is not meaningful
•Production teams often restrict SMOTE to borderline examples near decision boundaries rather than synthesizing from all minority points to reduce noise
📌 Examples
Payment fraud with continuous features: synthesizing between transaction amount of 500 dollars and 800 dollars, and velocity of 10 and 15 transactions per hour
Medical diagnosis with lab values: interpolating between two patients with diabetes, one with glucose 180 mg/dL and another with 220 mg/dL to generate synthetic training cases
SMOTE fails on text classification: interpolating sparse 10,000 dimension document vectors creates dense vectors that do not match real document structure