Undersampling

Undersampling is a technique used in quantitative finance and data science to address imbalanced data sets, particularly in machine learning applications. It involves reducing the number of observations in the majority class to balance the class distribution, aiming to prevent machine learning models from being biased towards the more frequent class⁴⁸, ⁴⁹. This process helps in creating a more symmetrical dataset for training, which can improve the model's ability to accurately identify instances of the minority class, such as rare events or anomalies⁴⁶, ⁴⁷.

History and Origin

The concept of undersampling emerged within the broader field of machine learning as researchers and practitioners began to grapple with the challenges posed by imbalanced datasets. Many real-world classification problems, such as fraud detection or disease diagnosis, inherently involve datasets where one class significantly outnumbers the others⁴⁴, ⁴⁵. Early machine learning algorithms, when trained on such skewed data, often exhibited a bias towards the majority class, leading to poor predictive performance on the minority class, which was often the class of primary interest⁴², ⁴³.

To counter this, techniques for resampling data were developed. Undersampling, along with its counterpart oversampling, became a fundamental strategy. While a single definitive "origin date" or "inventor" for undersampling is not cited, its development has been a continuous process within the evolution of machine learning and statistical analysis from the late 20th century onwards. The understanding and application of undersampling methods evolved as computational power increased and machine learning became more sophisticated, with various specific algorithms (e.g., Random Undersampling, Tomek Links) being proposed to refine the process and mitigate its drawbacks. Academic research continues to explore and refine these techniques, as evidenced by ongoing studies into their application and efficacy in various domains⁴¹.

Key Takeaways

• Undersampling is a data preprocessing technique used to balance datasets where one class is significantly more prevalent than others.⁴⁰
• It works by reducing the number of observations in the majority class to match or approach the number of observations in the minority class.³⁹
• The primary goal of undersampling is to prevent machine learning models from becoming biased towards the majority class, thereby improving their ability to correctly identify rare events.³⁸
• A key limitation of undersampling is the potential loss of valuable information from the discarded majority class samples.³⁷
• Undersampling is often employed in fields like finance for tasks such as fraud detection and credit risk assessment, where imbalanced data is common.³⁶

Interpreting Undersampling

Undersampling is interpreted as a strategic intervention to prepare big data for more effective predictive modeling. When a dataset is highly imbalanced—for instance, 99% legitimate transactions and 1% fraudulent ones—a machine learning model might achieve high overall accuracy simply by predicting "legitimate" for every transaction. However, this high accuracy would be misleading because the model would fail to identify the critical minority class (fraud).

B³⁵y applying undersampling, data scientists aim to create a training environment where the model is exposed to a more equitable representation of both classes. This rebalancing allows the algorithm to learn the distinguishing characteristics of the minority class more effectively, leading to improved model performance metrics such as recall and F1-score for the minority class, even if overall accuracy might slightly decrease. Th³³, ³⁴e success of undersampling is assessed by how well the balanced model performs on unseen, real-world imbalanced data, particularly in its ability to correctly classify the rare, important events.

Hypothetical Example

Consider a hypothetical online payment processor that uses machine learning to detect fraudulent transactions. Over a month, the system processes 10 million transactions. Of these, 9,990,000 are legitimate, while only 10,000 are fraudulent. This represents a severe imbalanced data set, with a ratio of approximately 999 legitimate transactions for every 1 fraudulent one.

If a classification model were trained on this raw data, it might learn to classify almost all transactions as legitimate, achieving over 99.9% accuracy. However, its ability to detect actual fraud (the minority class) would be very poor, leading to significant financial losses.

To address this, the data science team decides to apply undersampling. They randomly select 10,000 legitimate transactions from the 9,990,000 available. They then combine these 10,000 legitimate transactions with the 10,000 fraudulent transactions. The new, undersampled dataset now consists of 20,000 transactions, with an even 1:1 ratio of legitimate to fraudulent cases.

The machine learning model is then trained on this balanced 20,000-transaction dataset. By learning from an equal representation of both classes, the model is compelled to identify the subtle patterns that differentiate fraudulent activities from legitimate ones. While the training dataset is much smaller, the balanced nature allows the model to develop stronger predictive capabilities for detecting fraud, which is the primary objective of the system.

Practical Applications

Undersampling is a critical technique with several practical applications in finance, primarily where imbalanced data challenges classification and risk management models.

• Fraud Detection: This is one of the most prominent uses. Financial fraud, whether in credit card transactions, insurance claims, or banking, is inherently rare compared to legitimate activities. Un³¹, ³²dersampling helps create balanced datasets for training models to accurately identify these infrequent but high-impact fraudulent instances, allowing institutions to develop more robust fraud detection systems. Fo²⁹, ³⁰r example, studies have shown that undersampling techniques can enhance the ability of machine learning models to identify fraud in financial transactions.
• ²⁸ Credit Risk Modeling: In assessing credit risk, the number of loan defaults or bankruptcies is typically much lower than the number of non-defaults. Un²⁷dersampling can be applied to balance datasets used for building models that predict default probabilities, ensuring the model effectively learns the characteristics of defaulting borrowers. Re²⁶search indicates that undersampling can improve the performance of assessing credit risk.
• ²⁵ Anomaly Detection: Beyond fraud and credit, any financial scenario involving rare but significant events (e.g., unusual market movements, system breaches) can benefit. Undersampling can help train models to recognize these anomalies that deviate from the overwhelming majority of normal operations.
• Regulatory Compliance: Accurate predictive models are often crucial for complying with financial regulations. By improving the performance of models on minority classes, undersampling contributes to more reliable and fair predictive modeling, which can be important for regulatory scrutiny.

Limitations and Criticisms

While undersampling is an effective technique for addressing imbalanced data, it comes with notable limitations and criticisms, primarily centered around the potential for information loss.

The most significant drawback of undersampling is that by randomly removing samples from the majority class, it can lead to the loss of potentially valuable information. Th²³, ²⁴e discarded data points might contain crucial patterns, features, or relationships that are vital for the overall understanding of the dataset and for the model performance when applied to real-world, highly imbalanced data. Th²¹, ²²is information loss can result in a model that is less accurate or generalizes poorly to new, unseen data, even if it performs well on the undersampled training set.

C²⁰ritics argue that simply deleting data points, especially without a sophisticated selection mechanism, might introduce bias or fail to capture the full complexity of the majority class distribution. Fo¹⁸, ¹⁹r instance, if the majority class contains diverse subgroups, random undersampling might disproportionately remove instances from certain subgroups, leading to a skewed representation. This could make the model less robust in real-world scenarios where the full diversity of the majority class is present. A survey on imbalanced data classification highlights that conventional undersampling approaches may lead to the loss of essential information.

F¹⁷urthermore, determining the optimal undersampling ratio can be challenging. If too many majority class samples are removed, the model might struggle to learn what constitutes a "normal" event, potentially leading to an increased false positive rate for the minority class.

Undersampling vs. Oversampling

Undersampling and oversampling are two primary strategies used in machine learning to mitigate the effects of imbalanced data in classification problems. While both aim to balance the class distribution, they achieve this through fundamentally opposite approaches.

The confusion between the two often arises because their goal is the same: to create a more balanced dataset for model training. However, the choice between undersampling and oversampling (or a hybrid approach) depends heavily on the specific characteristics of the dataset, the severity of the imbalance, and the potential impact of information loss versus the risk of overfitting.

#⁷, ⁸# FAQs

What is the main purpose of undersampling?

The main purpose of undersampling is to address imbalanced data in machine learning by reducing the number of samples in the majority class. This helps prevent classification models from developing a bias towards the more common class, thereby improving their ability to accurately predict the less frequent (minority) class.

##⁵, ⁶# When should undersampling be used?
Undersampling is generally considered when the total size of the dataset is very large, and reducing the number of majority class samples still leaves enough data to adequately train the model. It's particularly useful in scenarios like fraud detection where the minority class is of critical interest, and computational efficiency is also a concern.

##⁴# What are the types of undersampling?
Common types of undersampling methods include Random Undersampling (randomly removing majority class samples), Tomek Links (removing majority class samples that are very close to minority class samples), and Cluster Centroids (replacing clusters of majority class samples with their centroids). These methods aim to reduce the majority class while trying to preserve important information.

Does undersampling lead to data loss?

Yes, a primary concern with undersampling is the potential for data loss. By removing observations from the majority class, valuable information or distinct patterns within that class might be discarded, which could negatively impact the overall model performance and its ability to generalize to new data.

##², ³# Can undersampling be combined with other techniques?
Yes, undersampling is often combined with other techniques, including oversampling methods, in hybrid approaches to mitigate the downsides of each. For example, some strategies combine undersampling to clean noisy majority samples with oversampling to generate synthetic minority samples, aiming for a more robust and balanced dataset.¹