What Are Examples of Dealing With Highly Imbalanced Data?


    What Are Examples of Dealing With Highly Imbalanced Data?

    When dealing with highly imbalanced data, strategies vary widely across different fields and scenarios, as evidenced by an Expert Data Scientist's approach to post-pandemic sales forecasting. Alongside industry leaders, we've also compiled additional answers that reflect a variety of tactics. From employing anomaly detection techniques to considering synthetic oversampling for fairness, this article delves into seven strategies for tackling data imbalance.

    • Sales Forecasting Post-Pandemic
    • Enhance Fraud Detection with SMOTE
    • Undersampling for Balanced Data
    • Employ Anomaly Detection Techniques
    • Prioritize Precision and Recall Metrics
    • Adjust Class Weights for Balance
    • Synthetic Oversampling for Fairness

    Sales Forecasting Post-Pandemic

    Sales forecasting has never been a simple chore, whether you're selling B2B, to consumers, or internally in a corporate setting. The COVID-19 pandemic has made it even more difficult as budgets are cut and individuals cling to their money. The post-pandemic effect on sales forecasting can be significant since it requires understanding how consumer behavior, market dynamics, and economic factors alter in the aftermath of a major disruptive event such as a pandemic.

    Let's look at a case study of dealing with significantly unbalanced data in sales prediction post-pandemic. In the months of May-June 2023, I was involved in designing a Machine Intelligence Model to predict sales revenue based on historical data from the years 2018 to 2023, including the pandemic period. Here's how we can approach it step by step.

    Data Collection and Analysis

    • Collect sales data from multiple sources, such as historical records, customer demographics, marketing initiatives, and economic factors.
    • Collect pandemic-specific data, including lockdowns, government regulations, and consumer attitude polls.
    • Define the target variable, such as sales volume, revenue, or other sales success metrics.

    Exploratory Data Analysis (EDA)

    • Data Exploration: Analyze sales data over time for patterns, seasonality, and pandemic-related abnormalities.
    • Imbalance Analysis: Analyze imbalance by examining the target variable's class distribution. For example, if sales dropped significantly during the epidemic, the data could be severely skewed.

    To balance your unbalanced datasets, hence enhancing your classification models, the model configuration panel provides several options for this purpose.

    Resampling Techniques

    • Oversampling: To balance the dataset, use techniques like SMOTE or ADASYN to increase the minority class (e.g., low sales during the pandemic).
    • Undersampling: In order to balance the dataset and guarantee representative samples of both classes, reduce the majority class (such as typical sales).
    • Hybrid Methods: To create a balanced dataset that retains crucial information, combine undersampling and oversampling techniques.

    Weights are another method for not dismissing any information and instead focusing on the source of the problem. That is, weighing instances based on their importance in your situation. Weighting made us aware of projected class outcomes that are underrepresented in the input data and would otherwise be obscured by overrepresented values.

    Dr. Manash Sarkar
    Dr. Manash SarkarExpert Data Scientist, Limendo GmbH

    Enhance Fraud Detection with SMOTE

    Dealing with highly imbalanced data is a common challenge in data science, particularly when building predictive models. A specific example where I faced this issue was in developing a fraud detection system for an online retail company. In this scenario, the instances of fraud were significantly less frequent than legitimate transactions, which is typical in fraud detection, leading to an imbalanced dataset.

    To effectively manage this, I employed several techniques to balance the dataset and improve model performance. The primary method was using the Synthetic Minority Over-sampling Technique (SMOTE). SMOTE works by creating synthetic samples from the minority class (in this case, fraudulent transactions) instead of creating copies. This technique helps in overcoming the overfitting problem, which is common when simply duplicating minority class examples.

    Additionally, I adjusted the classification algorithms to penalize misclassifications of the minority class more than the majority class. This approach, known as cost-sensitive learning, ensures that the model pays more attention to the minority class during training.

    Implementing these strategies not only balanced the data but also significantly improved the precision and recall of the model, leading to more reliable fraud detection. The company was able to decrease the number of fraudulent transactions slipping through the detection process without increasing the false positives, which are costly and disrupt customer satisfaction. This example underscores the importance of applying specialized techniques in scenarios of data imbalance to ensure the effectiveness of predictive modeling.

    Bruno Gavino
    Bruno GavinoFounder, CEO, CodeDesign

    Undersampling for Balanced Data

    When faced with highly imbalanced data, one technique involves reducing the number of examples from the majority class to equal the minority class. This process helps in creating a more balanced dataset, which can improve the performance of machine learning models. The main objective is to prevent the model from being overwhelmed by the majority class, allowing it to learn more about the less represented class.

    However, it's important to be cautious as this can also lead to the loss of potentially valuable information. To get started, one might explore various undersampling methods that suit their data best.

    Employ Anomaly Detection Techniques

    Another method to address data imbalance is the use of anomaly detection. This approach treats the minority class as anomalies, which can be beneficial, especially in scenarios like fraud detection where the event is rare. Anomaly detection techniques often focus on patterns that do not conform to expected behavior.

    By using this strategy, models are trained to identify rare events more accurately. If you're working on a problem with highly skewed data distributions, consider exploring anomaly detection algorithms specifically designed for such cases.

    Prioritize Precision and Recall Metrics

    A useful approach when evaluating models trained on imbalanced datasets is to prioritize the precision and recall metrics over mere accuracy. Accuracy alone may be misleading because it can be disproportionately influenced by the majority class. Precision and recall provide more insight into how well the model is identifying the minority class, which is often of greater interest.

    It's critical to examine these metrics to ensure that the model is truly effective for the task at hand. Before finalizing your model, make sure to check the precision-recall curves to better understand its performance.

    Adjust Class Weights for Balance

    Modifying class weights during the training of machine learning models is a powerful technique for handling data imbalance. This method involves giving more weight to the minority class, thus increasing the cost of misclassifying samples from that class. By doing so, the model pays more attention to the minority class and improves its ability to predict those cases correctly.

    This balances the scale for minority classes without having to alter the dataset directly. When setting up your next machine learning experiment, adjust the class weights to see if it enhances your model's predictive power.

    Synthetic Oversampling for Fairness

    Implementing synthetic minority oversampling is a widely recognized solution for dealing with imbalanced datasets. It generates new, synthetic examples in the minority class to attain a balanced class distribution. This technique helps the model to learn patterns from the minority class more effectively without losing information, which can be a downside of undersampling.

    Oversampling can lead to a more robust and fair model which performs well on unseen data. If your data is skewed, consider generating synthetic samples to improve your model’s ability to generalize.