What are the best practices for dealing with imbalanced datasets?

Imbalanced datasets are a common challenge in data mining, especially when dealing with classification problems. They occur when one class of the target variable has significantly more or less instances than the others, leading to biased models and poor performance. In this article, you will learn some of the best practices for dealing with imbalanced datasets and how to apply them to your data mining projects.

1 Understand the problem

The first step is to understand the nature and extent of the imbalance in your dataset. You can use descriptive statistics, visualizations, and domain knowledge to explore the distribution and characteristics of the classes. You should also evaluate the impact of the imbalance on your business or research objectives, and the trade-offs between different metrics and costs. For example, if you are predicting fraud, you may want to prioritize recall over precision, or assign higher penalties for false negatives than false positives.

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Ilhan Karic

Machine Learning Engineer
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Well you certainly want to see where the imbalance is coming from before doing any balancing. I generally try to use domain knowledge or ask someone who has domain knowledge of the process. You generally don't want to balance in situations when you are predicting frequencies or probabilities. I use descriptive statistics and data distribution visualization of both features and targets to get a sense of what is going on. Other than this, personally I prefer undersampling because it is easier to do. If I am doing oversampling I generally train a variational autoencoder on the minority class and use it to generate synthetic data.

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2 Choose an appropriate algorithm

Some algorithms are more sensitive to imbalanced datasets than others, and may require tuning or modification to handle them effectively. For instance, decision trees and ensemble methods tend to be more robust, while logistic regression and neural networks may need regularization or class weighting. You should also consider the type and complexity of the data, and the assumptions and limitations of each algorithm. For example, if your data is linearly separable, you may use a support vector machine with a kernel trick, but if your data is noisy or nonlinear, you may need a more flexible model.

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Ilhan Karic

Machine Learning Engineer
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Descriptive statistics to get an idea / feeling of the underlying distributions within the data and then I go from there. Generally I leave neural networks for when everything else fails. I prefer interpretability over super low error rates if they are within allowed margins. XGB is my favorite algorithm when doing stuff with decision trees. It is very easily interpretable and you don't need to scale or transform features in any way for it to work.

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3 Apply resampling techniques

Resampling techniques are methods to modify the original dataset by adding, removing, or changing instances to create a more balanced distribution of the classes. There are two main types of resampling: oversampling and undersampling. Oversampling involves creating new instances of the minority class, either by duplicating existing ones or by generating synthetic ones using algorithms like SMOTE or ADASYN. Undersampling involves reducing the number of instances of the majority class, either by randomly selecting a subset or by using algorithms like Tomek links or cluster centroids. Resampling techniques can help improve the model performance and generalization, but they also have some drawbacks, such as overfitting, information loss, or computational cost.

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4 Use stratified sampling and cross-validation

Stratified sampling and cross-validation are techniques to ensure that the train and test sets have a similar proportion of the classes as the original dataset. This is important to avoid creating biased or unrepresentative splits that may affect the model evaluation and selection. Stratified sampling divides the dataset into subsets that maintain the same class distribution as the whole dataset, and then randomly assigns them to the train and test sets. Cross-validation is a method to split the dataset into k folds, and then use one fold as the test set and the rest as the train set, repeating this process k times and averaging the results. Both techniques can help reduce the variance and increase the reliability of the model estimates.

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5 Combine multiple approaches

There is no single best practice for dealing with imbalanced datasets, as different situations may require different solutions. Therefore, it is advisable to combine multiple approaches and experiment with different combinations and parameters to find the optimal balance. For example, you may use a combination of resampling, algorithm selection, and metric selection to optimize your model performance and address the specific challenges of your problem. You may also use techniques like feature selection, feature engineering, or dimensionality reduction to enhance the quality and relevance of the data.

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Ilhan Karic

Machine Learning Engineer
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A very important thing is to use feature engineering with regards to the domain knowledge that exists in the certain field we are working on. I have seen experts doing predictions manually and they will tell you "I use this parameter as a predictor myself." And when you ask "How do you come up with this?" they will generally give you a formula or something that computes or estimates it based on other available data. More often than not when I computed that and fed it as a feature in my algorithm when training I've gotten much better results than with just the features alone.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are the best practices for dealing with imbalanced datasets?

1

2

3

4

5

6

1 Understand the problem

2 Choose an appropriate algorithm

3 Apply resampling techniques

4 Use stratified sampling and cross-validation

5 Combine multiple approaches

6 Here’s what else to consider

Data Mining

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What are the best practices for dealing with imbalanced datasets?

1

2

3

4

5

6

1 Understand the problem

2 Choose an appropriate algorithm

3 Apply resampling techniques

4 Use stratified sampling and cross-validation

5 Combine multiple approaches

6 Here’s what else to consider

Data Mining

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