A Gentle Introduction to Imbalanced Classification

Classification predictive modeling involves predicting a class label for a given observation.

An imbalanced classification problem is an example of a classification problem where the distribution of examples across the known classes is biased or skewed.

The distribution can vary from a slight bias to a severe imbalance where there is one example in the minority class for hundreds, thousands, or millions of examples in the majority class or classes.

Imbalanced classifications pose a challenge for predictive modeling as most of the machine learning algorithms used for classification were designed around the assumption of an equal number of examples for each class.

This results in models that have poor predictive performance, specifically for the minority class.

This is a problem because typically, the minority class is more important and therefore the problem is more sensitive to classification errors for the minority class than the majority class.

In this tutorial, you will discover imbalanced classification predictive modeling.

After completing this tutorial, you will know:Let’s get started.

A Gentle Introduction to Imbalanced ClassificationPhoto by John Mason, some rights reserved.

This tutorial is divided into five parts; they are:Classification is a predictive modeling problem that involves assigning a class label to each observation.

… classification models generate a predicted class, which comes in the form of a discrete category.

For most practical applications, a discrete category prediction is required in order to make a decision.

— Page 248, Applied Predictive Modeling, 2013.

Each example is comprised of both the observations and a class label.

For example, we may collect measurements of a flower and classify the species of flower (label) from the measurements.

The number of classes for a predictive modeling problem is typically fixed when the problem is framed or described, and typically, the number of classes does not change.

We may alternately choose to predict a probability of class membership instead of a crisp class label.

This allows a predictive model to share uncertainty in a prediction across a range of options and allow the user to interpret the result in the context of the problem.

Like regression models, classification models produce a continuous valued prediction, which is usually in the form of a probability (i.


, the predicted values of class membership for any individual sample are between 0 and 1 and sum to 1).

— Page 248, Applied Predictive Modeling, 2013.

For example, given measurements of a flower (observation), we may predict the likelihood (probability) of the flower being an example of each of twenty different species of flower.

The number of classes for a predictive modeling problem is typically fixed when the problem is framed or described, and usually, the number of classes does not change.

A classification predictive modeling problem may have two class labels.

This is the simplest type of classification problem and is referred to as two-class classification or binary classification.

Alternately, the problem may have more than two classes, such as three, 10, or even hundreds of classes.

These types of problems are referred to as multi-class classification problems.

When working on classification predictive modeling problems, we must collect a training dataset.

A training dataset is a number of examples from the domain that include both the input data (e.


measurements) and the output data (e.


class label).

Depending on the complexity of the problem and the types of models we may choose to use, we may need tens, hundreds, thousands, or even millions of examples from the domain to constitute a training dataset.

The training dataset is used to better understand the input data to help best prepare it for modeling.

It is also used to evaluate a suite of different modeling algorithms.

It is used to tune the hyperparameters of a chosen model.

And finally, the training dataset is used to train a final model on all available data that we can use in the future to make predictions for new examples from the problem domain.

Now that we are familiar with classification predictive modeling, let’s consider an imbalance of classes in the training dataset.

The number of examples that belong to each class may be referred to as the class distribution.

Imbalanced classification refers to a classification predictive modeling problem where the number of examples in the training dataset for each class label is not balanced.

That is, where the class distribution is not equal or close to equal, and is instead biased or skewed.

For example, we may collect measurements of flowers and have 80 examples of one flower species and 20 examples of a second flower species, and only these examples comprise our training dataset.

This represents an example of an imbalanced classification problem.

An imbalance occurs when one or more classes have very low proportions in the training data as compared to the other classes.

— Page 419, Applied Predictive Modeling, 2013.

We refer to these types of problems as “imbalanced classification” instead of “unbalanced classification“.

Unbalance refers to a class distribution that was balanced and is now no longer balanced, whereas imbalanced refers to a class distribution that is inherently not balanced.

There are other less general names that may be used to describe these types of classification problems, such as:The imbalance of a problem is defined by the distribution of classes in a specific training dataset.

… class imbalance must be defined with respect to a particular dataset or distribution.

Since class labels are required in order to determine the degree of class imbalance, class imbalance is typically gauged with respect to the training distribution.

— Page 16, Imbalanced Learning: Foundations, Algorithms, and Applications, 2013.

It is common to describe the imbalance of classes in a dataset in terms of a ratio.

For example, an imbalanced binary classification problem with an imbalance of 1 to 100 (1:100) means that for every one example in one class, there are 100 examples in the other class.

Another way to describe the imbalance of classes in a dataset is to summarize the class distribution as percentages of the training dataset.

For example, an imbalanced multiclass classification problem may have 80 percent examples in the first class, 18 percent in the second class, and 2 percent in a third class.

Now that we are familiar with the definition of an imbalanced classification problem, let’s look at some possible reasons as to why the classes may be imbalanced.

The imbalance to the class distribution in an imbalanced classification predictive modeling problem may have many causes.

There are perhaps two main groups of causes for the imbalance we may want to consider; they are data sampling and properties of the domain.

It is possible that the imbalance in the examples across the classes was caused by the way the examples were collected or sampled from the problem domain.

This might involve biases introduced during data collection, and errors made during data collection.

For example, perhaps examples were collected from a narrow geographical region, or slice of time, and the distribution of classes may be quite different or perhaps even collected in a different way.

Errors may have been made when collecting the observations.

One type of error might have been applying the wrong class labels to many examples.

Alternately, the processes or systems from which examples were collected may have been damaged or impaired to cause the imbalance.

Often in cases where the imbalance is caused by a sampling bias or measurement error, the imbalance can be corrected by improved sampling methods, and/or correcting the measurement error.

This is because the training dataset is not a fair representation of the problem domain that is being addressed.

The imbalance might be a property of the problem domain.

For example, the natural occurrence or presence of one class may dominate other classes.

This may be because the process that generates observations in one class is more expensive in time, cost, computation, or other resources.

As such, it is often infeasible or intractable to simply collect more samples from the domain in order to improve the class distribution.

Instead, a model is required to learn the difference between the classes.

Now that we are familiar with the possible causes of a class imbalance, let’s consider why imbalanced classification problems are challenging.

The imbalance of the class distribution will vary across problems.

A classification problem may be a little skewed, such as if there is a slight imbalance.

Alternately, the classification problem may have a severe imbalance where there might be hundreds or thousands of examples in one class and tens of examples in another class for a given training dataset.

Most of the contemporary works in class imbalance concentrate on imbalance ratios ranging from 1:4 up to 1:100.

[…] In real-life applications such as fraud detection or cheminformatics we may deal with problems with imbalance ratio ranging from 1:1000 up to 1:5000.

— Learning from imbalanced data – Open challenges and future directions, 2016.

A slight imbalance is often not a concern, and the problem can often be treated like a normal classification predictive modeling problem.

A severe imbalance of the classes can be challenging to model and may require the use of specialized techniques.

Any dataset with an unequal class distribution is technically imbalanced.

However, a dataset is said to be imbalanced when there is a significant, or in some cases extreme, disproportion among the number of examples of each class of the problem.

— Page 19, Learning from Imbalanced Data Sets, 2018.

The class or classes with abundant examples are called the major or majority classes, whereas the class with few examples (and there is typically just one) is called the minor or minority class.

When working with an imbalanced classification problem, the minority class is typically of the most interest.

This means that a model’s skill in correctly predicting the class label or probability for the minority class is more important than the majority class or classes.

Developments in learning from imbalanced data have been mainly motivated by numerous real-life applications in which we face the problem of uneven data representation.

In such cases the minority class is usually the more important one and hence we require methods to improve its recognition rates.

— Learning from imbalanced data – Open challenges and future directions, 2016.

The minority class is harder to predict because there are few examples of this class, by definition.

This means it is more challenging for a model to learn the characteristics of examples from this class, and to differentiate examples from this class from the majority class (or classes).

The abundance of examples from the majority class (or classes) can swamp the minority class.

Most machine learning algorithms for classification predictive models are designed and demonstrated on problems that assume an equal distribution of classes.

This means that a naive application of a model may focus on learning the characteristics of the abundant observations only, neglecting the examples from the minority class that is, in fact, of more interest and whose predictions are more valuable.

… the learning process of most classification algorithms is often biased toward the majority class examples, so that minority ones are not well modeled into the final system.

— Page vii, Learning from Imbalanced Data Sets, 2018.

Imbalanced classification is not “solved.

”It remains an open problem generally, and practically must be identified and addressed specifically for each training dataset.

This is true even in the face of more data, so-called “big data,” large neural network models, so-called “deep learning,” and very impressive competition-winning models, so-called “xgboost.

”Despite intense works on imbalanced learning over the last two decades there are still many shortcomings in existing methods and problems yet to be properly addressed.

— Learning from imbalanced data – Open challenges and future directions, 2016.

Now that we are familiar with the challenge of imbalanced classification, let’s look at some common examples.

Many of the classification predictive modeling problems that we are interested in solving in practice are imbalanced.

As such, it is surprising that imbalanced classification does not get more attention than it does.

Imbalanced learning not only presents significant new challenges to the data research community but also raises many critical questions in real-world data- intensive applications, ranging from civilian applications such as financial and biomedical data analysis to security- and defense-related applications such as surveillance and military data analysis.

— Page 2, Imbalanced Learning: Foundations, Algorithms, and Applications, 2013.

Below is a list of ten examples of problem domains where the class distribution of examples is inherently imbalanced.

Many classification problems may have a severe imbalance in the class distribution; nevertheless, looking at common problem domains that are inherently imbalanced will make the ideas and challenges of class imbalance concrete.

The list of examples sheds light on the nature of imbalanced classification predictive modeling.

Each of these problem domains represents an entire field of study, where specific problems from each domain can be framed and explored as imbalanced classification predictive modeling.

This highlights the multidisciplinary nature of class imbalanced classification, and why it is so important for a machine learning practitioner to be aware of the problem and skilled in addressing it.

Imbalance can be present in any data set or application, and hence, the practitioner should be aware of the implications of modeling this type of data.

— Page 419, Applied Predictive Modeling, 2013.

Notice that most, if not all, of the examples are likely binary classification problems.

Notice too that examples from the minority class are rare, extreme, abnormal, or unusual in some way.

Also notice that many of the domains are described as “detection,” highlighting the desire to discover the minority class amongst the abundant examples of the majority class.

We now have a robust overview of imbalanced classification predictive modeling.

This section provides more resources on the topic if you are looking to go deeper.

In this tutorial, you discovered imbalanced classification predictive modeling.

Specifically, you learned:Do you have any questions?.Ask your questions in the comments below and I will do my best to answer.


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