10 Ways to Handle Imbalanced Data in a Classification Problem

10 Ways to Handle Imbalanced Data in a Classification Problem

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Atul
Atul Harsha
Senior Manager Content
Updated on Sep 20, 2023 13:44 IST

Imbalanced datasets, where one class greatly outnumbers others, pose machine learning challenges. To address this, techniques like oversampling, undersampling, SMOTE, ADASYN, Tomek links, ENN, CNN, near miss, and one-sided selection can be employed. Each has its merits and drawbacks, so evaluate them carefully to choose the best fit for your dataset and task.

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When working on classification problems in machine learning, dealing with imbalanced data is a common hurdle. This imbalance happens when one group in your data vastly outnumbers the others, making it tough for your model to make accurate predictions. In this blog, we’ll dive into 10 practical methods to handle imbalanced data issues. Whether you’re working on fraud detection, medical diagnoses, or any other classification task, these techniques will help you balance the scales and enhance the performance of your machine-learning models.

What is Imbalanced Data?

Imbalanced data is a dataset where the classes are not equally represented. This can be a problem in machine learning because traditional models tend to perform poorly on the minority class. In other words, Imbalanced data refers to a dataset where the classes are not equally represented. For example, in a binary classification problem with two classes (class A and class B), if the number of samples belonging to class A is significantly larger than the number of samples belonging to class B, the dataset is imbalanced.

Why is Imbalanced Data a Problem?

One of the key challenges of imbalanced data is that traditional performance metrics, such as accuracy, can be misleading. For example, if the majority class represents 95% of the samples and the model always predicts the majority class, it will achieve an accuracy of 95%, even though it is not accurately predicting the minority class. This can make it difficult to accurately assess the model’s performance on the minority class.

Here are some of the problems that can arise when we don’t handle imbalanced data in a classification problem:

  • Poor model performance: Traditional machine learning models tend to perform poorly on the minority class in imbalanced datasets. This can lead to poor overall performance on the dataset and a high error rate for the minority class.
  • Misleading performance metrics: Traditional performance metrics, such as accuracy, can be misleading on imbalanced data. This can make it difficult to accurately assess the model’s performance on the minority class.
  • Bias towards the majority class: Imbalanced datasets can lead to a bias towards the majority class, as the model is trained on a majority of samples from the majority class. This can result in poor performance in the minority class.
  • Real-world implications: In many real-world applications, it is important to accurately classify samples from the minority class. If the model performs poorly in the minority class, it can have serious consequences.

There are several techniques that can be used to handle imbalanced data in a classification problem, such as collecting more data for the minority class, using stratified sampling, rebalancing the data, using a weighted loss function, and using algorithms that are robust to imbalanced data. It is important to carefully evaluate the effectiveness of each approach on your specific dataset and task.

How to Handle Imbalanced Data in Machine Learning?

Here are 10 techniques which can be used to handle imbalanced data in machine learning:

1. Oversampling: A Simple Trick to Balance Your Data

In the world of data analysis, it’s crucial to have a balanced set of data to get accurate results. “Oversampling” is a handy technique to achieve this balance. It involves making copies of the smaller group of data until it’s as large as the bigger group. This way, the analysis is fair and gives us better results.

Example: Handle imbalanced data using Oversampling in Detecting Fraud:

Let’s say we have a set of bank transactions, labeled as “fraud” or “non-fraud”. At first, we have 100 “non-fraud” cases and only 20 “fraud” cases, which is not balanced. To fix this, we can copy some of the “fraud” cases until we have more, like 40, making the data more even. This helps in creating a system that can spot fraud more accurately.

2. Under-sampling: A Strategy to Balance Your Data by Reducing Excess

In data analysis, sometimes we have too much information from one group, which can skew the results. A technique called “under-sampling” can help fix this. It means we remove some examples from the larger group until it matches the size of the smaller group. This ensures a balanced view, helping to create a more reliable analysis.

Example: Handle imbalanced data using Under-sampling in Fraud Detection:

Imagine in the bank transaction dataset initially, we had 100 “non-fraud” cases and only 20 “fraud” cases. To make the data more balanced, we can remove some “non-fraud” cases until both groups are equal in size, say 20 each. This adjustment helps in building a system that can identify fraud with greater accuracy.

3. Hybrid Sampling: Mixing Two Methods for Better Data

When we work with data, it’s important to have a balanced mix to get the best results. “Hybrid sampling” helps us do just that. It’s a method where we add more examples to the smaller group and take away some from the bigger group. This way, we have a fair and even set of data to work with.

Example: Handling imbalanced data using Hybrid Sampling to Spot Fraud

Let’s take the same example where bank transactions are labelled as “fraud” or “non-fraud”. At the start, there are many more “non-fraud” cases than “fraud” cases. To make things more balanced, we can add more examples to the “fraud” group and remove some from the “non-fraud” group. This helps us have an equal number of cases in both groups, making it easier to spot fraud accurately.

4. SMOTE: Creating New Examples to Balance Data

While analyzing the data sometimes we need to add more examples to the smaller group to make the data balanced. “SMOTE”, which stands for Synthetic Minority Oversampling Technique, is a method that helps us do this. It picks two similar examples from the smaller group and makes a new example that is a mix of the two.

Example: Handle imbalanced data using SMOTE to Help Find Fraud

Let’s consider a bank transaction case where we don’t have many “fraud” examples. To fix this, SMOTE can be used to create new “fraud” examples. It does this by finding two “fraud” cases that are similar and then making a new case that is a blend of these two. This way, we have more “fraud” examples to work with, helping us to spot fraud more effectively.

5. ADASYN: Adjusting the Data Balance Smartly

When we are dealing with data, sometimes the groups are not evenly matched, with one group having much more data than the other. “ADASYN”, which stands for Adaptive Synthetic Sampling, is a smart tool that helps to even things out. It creates new examples in the smaller group, and the number of new examples it makes depends on how imbalanced the data is to start with.

Example: Handle imbalanced data using ADASYN to Spot Fraud More Accurately

Let’s say in bank transactions we have a lot more “non-fraud” cases compared to “fraud” cases, like a 10:1 ratio, ADASYN steps in to create more “fraud” examples. It will create even more new examples if the difference between “non-fraud” and “fraud” cases is larger, helping us to have a better balance and spot fraud more accurately.

6. Tomek Links: A Method to Fine-Tune Your Data

Sometimes while analyzing the data necessary to remove some data points to get a clearer picture. “Tomek Links” is a technique that helps us do this. It finds pairs of data where one is from the larger group and the other is from the smaller group, and they are very similar. Then, it removes the data point from the larger group to make the data more balanced.

Example: Using Tomek Links to Improve Fraud Detection

Imagine in the bank transactions dataset, we find pairs of transactions where one is “fraud” and the other is “non-fraud”, and they are quite similar. To make our data better, we remove the “non-fraud” transaction from the pair in the dataset. This way, our system can focus more on the distinct features of the “fraud” transactions, helping to identify fraud more accurately.

7. ENN Rule: Cleaning Up Data with the Help of Neighbors

When analyzing data, it’s crucial to have a clean and balanced dataset to get accurate results. The “Edited Nearest Neighbor” or ENN rule helps us achieve this. It works by spotting and removing data points from the larger group that are similar to points in the smaller group, helping to clear up any confusion and make the data more reliable.

Example: Enhancing Fraud Detection with the ENN Rule

In the bank transaction dataset, we might find a “non-fraud” transaction that is very similar to a couple of “fraud” transactions. To improve our data, we use the ENN rule to remove this “non-fraud” transaction. This way, we can focus more on the clear differences between “fraud” and “non-fraud” cases, helping us to spot fraud more effectively.

8. Condensed Nearest Neighbor Rule: Building a Focused Dataset

In data analysis, it’s often beneficial to create a focused dataset that zeroes in on the most relevant examples. The “Condensed Nearest Neighbor” rule helps us do this by forming a new dataset that includes only the most relevant examples from the larger group, along with all the examples from the smaller group. This way, we can concentrate on the data points that matter the most.

Example: Refining Fraud Detection with the Condensed Nearest Neighbor Rule

Imagine we have a dataset that labels bank transactions as either “fraud” or “non-fraud”. Using this rule, we find the “non-fraud” transactions that are most similar to the “fraud” transactions. We then create a new dataset that includes only these selected “non-fraud” transactions, along with all the “fraud” transactions. This approach helps us to focus on the most critical data points, potentially making our fraud detection methods more precise.

9. Near Miss Method: A Focused Approach to Balancing Imbalanced Datasets

Balancing imbalanced datasets is a crucial step in data analysis, especially when there is a significant disparity between the classes. The near-miss method is a technique designed to streamline this process. It works by identifying and removing data points from the majority class that are closely similar to those in the minority class, thus helping to create a more balanced and focused dataset.

Example: Streamlining Fraud Detection with the Near Miss Method

Imagine that we have a bank dataset with two classes: “fraud” and “non-fraud.” The “non-fraud” class is the majority class. We can use the Near Miss method to oversample the “fraud” class by following these steps:

  1. Identify the “non-fraud” transactions that are most similar to the “fraud” transactions. We can use a variety of methods to do this, such as K-NN or k-means clustering.
  2. Remove these “non-fraud” transactions from the dataset.

The resulting dataset will have a more balanced class distribution, with an oversampled “fraud” class. This can help to improve the accuracy of fraud detection systems.

Benefits of the Near Miss Method

The Near Miss method has a number of benefits, including:

  • It is a simple and effective oversampling technique.
  • Particularly useful for datasets where the minority class is very small.
  • Help to improve the accuracy of machine learning models by oversampling the minority class.
  • Removes similar data points from the majority class thereby increasing the accuracy.

10. One-Sided Selection: Balance the Imbalanced Data

One-sided selection is a technique for oversampling the minority class in an imbalanced dataset. It involves selecting a subset of the majority class that is most similar to the minority class. Then the rest of the majority class examples are removed from the dataset.

To do this, we first identify the majority class examples that are nearest to minority class examples. We can use a variety of methods to do this, such as k-nearest neighbors or k-means clustering.

Once we have identified the most similar majority class examples, we create a new dataset that consists of those majority class examples and all of the minority class examples. We then remove the rest of the majority class examples from the dataset.

Example:

Suppose we have a dataset with two classes, “fraud” and “non-fraud”. Here the majority class is “non-fraud”. We can use one-sided selection to oversample the “fraud” class by following these steps:

  1. Identify the “non-fraud” examples that are nearest to the “fraud” examples.
  2. Create a new dataset that consists of those “non-fraud” examples and all of the “fraud” examples.
  3. Remove the rest of the “non-fraud” examples from the dataset.

The resulting dataset will have a more balanced class distribution, with the “fraud” class being oversampled.

One-sided selection is a simple and effective oversampling technique. It is particularly useful for datasets where the minority class is very small.

Performance Metrics to Evaluate Imbalanced Classification

There are a number of performance measures that are commonly used to evaluate the performance of machine learning models for imbalanced classification tasks. These measures take into account the imbalanced nature of the dataset and aim to provide a more accurate assessment of the model’s performance.

1. Precision

Precision is the proportion of true positive predictions made by the model among all positive predictions. It is a useful measure for imbalanced classification because it takes into account the number of false positive predictions made by the model.

For example, consider a model that is trained to classify emails as spam or not spam. If the model makes 10 predictions of spam, but only 5 of those emails are actually spam, the precision would be 50% (5 true positives / 10 positive predictions).

3. Recall

Recall is the proportion of true positive predictions made by the model among all actual positive examples. It is a useful measure for imbalanced classification because it takes into account the number of false negative predictions made by the model.

For example, consider the same spam email classification task as above. If there are 100 spam emails in the dataset, but the model only correctly identifies 50 of them, the recall would be 50% (50 true positives / 100 actual positive examples).

4. F1 score

The F1 score is a balance between precision and recall, and it is calculated as the harmonic mean of precision and recall. It is a useful measure for imbalanced classification because it takes into account both false positive and false negative predictions made by the model.

For example, consider the same spam email classification task as above. If the precision is 50% and the recall is 50%, the F1 score would be 50% ((2 * 50% * 50%) / (50% + 50%)).

5. AUC-ROC

The AUC-ROC (area under the receiver operating characteristic curve) is a measure of the model’s ability to distinguish between positive and negative classes. It is calculated by plotting the true positive rate against the false positive rate at various classification thresholds. AUC-ROC is a useful measure for imbalanced classification because it is not affected by the imbalanced nature of the dataset.

For example, consider the same spam email classification task as above. If the model has a true positive rate of 50% (meaning it correctly identifies 50% of the spam emails) and a false positive rate of 1% (meaning it incorrectly classifies 1% of the non-spam emails as spam), the AUC-ROC would be a measure of how well the model can distinguish between spam and non-spam emails. AUC-ROC values range from 0 to 1, with a value of 1 indicating perfect classification and a value of 0.5 indicating random classification.

6. Accuracy

Accuracy is the proportion of correct predictions made by the model, but it can be misleading when the dataset is imbalanced because the model may simply predict the majority class most of the time and still have high accuracy. For example, consider the same spam email classification task as above. If the dataset is imbalanced, with 99% of emails being not spam and 1% being spam, the model could simply always predict “not spam” and have an accuracy of 99%, even if it is not making any correct predictions on the spam emails.

Read More: Difference Between Accuracy and Precision

Choosing the right performance measure

It is important to consider using multiple performance measures when evaluating the performance of a machine learning model for imbalanced classification tasks, as no single measure is sufficient on its own.

For example, if the model is being used to detect fraud, then it is important to have a high recall so that the model does not miss any fraudulent transactions. However, if the model is being used to classify customers as high-risk or low-risk, then it is important to have high precision so that the model does not incorrectly classify low-risk customers as high-risk.

Conclusion

In conclusion, imbalanced data is a common problem in machine learning that occurs when the classes in a dataset are not equally represented. Imbalanced data can lead to poor performance on the minority class and overall imbalanced performance on the dataset, and traditional performance metrics, such as accuracy, can be misleading.

In this blog, we already discussed several techniques that can be used to handle imbalanced data in a classification problem. It is important to carefully evaluate the effectiveness of each approach on your specific dataset and task. By handling imbalanced data, it is possible to improve the model’s performance on the minority class and achieve a more balanced overall performance on the dataset.

About the Author
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Atul Harsha
Senior Manager Content

Experienced AI and Machine Learning content creator with a passion for using data to solve real-world challenges. I specialize in Python, SQL, NLP, and Data Visualization. My goal is to make data science engaging an... Read Full Bio