2023-01-20

Classification with Imbalanced Data

What is Imbalanced Data

Imbalanced data refers to a situation in which the classes in a classification problem are not represented equally. For example, in a binary classification problem, if there are two classes 'A' and 'B', and 'A' has significantly more samples than 'B', the dataset is considered imbalanced.

Examples of imbalanced data can be found in various domains such as fraud detection in banking, where fraudulent transactions are rare compared to genuine transactions; medical diagnosis, where the occurrence of certain diseases is much lower than non-occurrences; and spam detection in emails.

The major challenge with imbalanced data is that many machine learning algorithms tend to be biased towards the majority class, leading to poor predictive performance for the minority class.

Data Approach to Tackle Imbalanced Data

When we talk about data approaches in the context of imbalanced datasets, we generally refer to methods that involve manipulating the dataset itself in order to alleviate the imbalance between classes. The aim is to achieve a more balanced class distribution, which in turn can enhance the performance of classifiers on the minority class.

Oversampling

Oversampling is a technique used to address class imbalance by increasing the number of instances from the minority class in the dataset. There are various ways to perform oversampling, including simply duplicating instances from the minority class or generating synthetic instances.

Synthetic Minority Over-sampling Technique (SMOTE)

SMOTE is one of the most popular algorithms for oversampling. It aims to generate synthetic samples in a way that is more intelligent than just duplicating instances. The algorithm works by selecting instances that are close in the feature space, drawing a line between the instances in the feature space and generating new instances that lie along that line.

Specifically, for each instance in the minority class, SMOTE selects k nearest neighbors, chooses one of them at random, and creates a synthetic instance at a random point between the two instances.

Using SMOTE can create a more general model since the synthetic instances are a kind of extrapolation. It forces the classifier to create more generalized decision boundaries rather than overfitting to the training data.

Pros and Cons of Oversampling

Pros

It can improve the classifier’s performance on the minority class.
By creating synthetic instances, the information richness is preserved.
It is generally more useful compared to undersampling when the dataset is small.

Cons

Oversampling, particularly with duplication, increases the risk of overfitting since it can cause the model to be too sensitive to specific instances.
It can be computationally expensive, especially when the dataset is large, as it increases the size of the training data.

Undersampling

In contrast to oversampling, undersampling reduces the number of instances in the majority class. The idea is to decrease the size of the majority class, bringing it closer to the size of the minority class.

Undersampling can be as simple as randomly removing instances from the majority class until a more balanced distribution is achieved. However, you need to use this approach carefully, as removing instances can lead to loss of information which might be valuable for the classifier.

Pros and Cons of Undersampling

Pros

It is computationally less expensive than oversampling as it reduces the size of the dataset.
It can be useful in reducing the likelihood of overfitting, especially in cases where the majority class is overrepresented.

Cons

The main drawback is the loss of potentially useful data.
If not done with care, it can cause the model to underfit.

Algorithm Approach to Tackle Imbalanced Data

Unlike data approaches that focus on manipulating the dataset itself, algorithmic approaches modify the underlying algorithm or model to make it more sensitive to the minority class.

Class Weight

Class weights are a set of coefficients that can be used during the training phase of a machine learning algorithm. By assigning different weights to classes, we can make the model more sensitive to the minority class. In essence, we are telling the algorithm to "pay more attention" to the minority class. This is typically done by assigning a higher weight to the minority class, and a lower weight to the majority class.

For example, in a binary classification problem with a severely imbalanced dataset, we might assign a weight of 10 to the minority class for every single instance of the majority class.

Implementing Class Weight

Many machine learning libraries and frameworks such as scikit-learn, TensorFlow, and XGBoost allow users to easily set class weights during model training. This is often done through a parameter such as class_weight which can be set when initializing the model.

Pros and Cons of Using Class Weight

Pros

Simple to implement, usually just a parameter setting in many machine learning libraries.
Does not alter the dataset itself, preserving all information.
Often effective in improving minority class performance.

Cons

Can result in a higher rate of false positives for the minority class, as the model becomes more sensitive to it.
The choice of class weights can be somewhat arbitrary and may require tuning.

One-Class Classification

One-Class Classification (OCC), also known as novelty or outlier detection, is an algorithmic approach where the model is trained using data from only the minority class (or the class of interest). The goal of the one-class classifier is to recognize instances that belong to a specific class (the minority class) and distinguish them from all other instances.

Implementing One-Class Classification

Popular algorithms for one-class classification include One-Class SVM and Isolation Forest. These algorithms are designed to learn the characteristics of the minority class and are capable of detecting new instances that do not conform to these learned characteristics.

Pros and Cons of One-Class Classification

Pros

Effective when the minority class is of primary interest.
Can handle very imbalanced datasets where the minority class instances are very scarce.
Robust to changes in the majority class.

Cons

Requires a sufficient number of minority class instances to learn its characteristics effectively.
Not suitable for cases where misclassification costs for both classes are comparable and important.

Hybrid Approaches

Hybrid approaches combine elements of both data-level and algorithm-level strategies. Hybrid approaches often aim to capitalize on the strengths of both methodologies.

While data and algorithm approaches can be effective in their own right, in some scenarios, they might not be sufficient to achieve the desired performance. For example, oversampling might lead to overfitting, while merely adjusting class weights might not sufficiently address the bias towards the majority class. By combining these approaches, we can sometimes achieve a more robust model that performs well on both majority and minority classes.

Common Hybrid Strategies

SMOTE with Class Weights

One common hybrid approach involves using SMOTE in conjunction with class weights. SMOTE helps by generating synthetic samples for the minority class, while class weights make the algorithm more sensitive to these synthetic samples. This combination can enhance the focus on the minority class without relying solely on synthetic data.

Ensemble Methods with Data Sampling

Ensemble methods, like Random Forests and Gradient Boosting, can be combined with data sampling techniques. For instance, an ensemble of decision trees where each tree is trained on a different subset of the data, with subsets created using a mix of oversampling and undersampling techniques, can sometimes achieve superior performance.

Implementing Hybrid Approaches

To implement a hybrid approach, you should experiment with different combinations of data and algorithmic strategies. This could involve integrating oversampling during data pre-processing and setting class weights during model training. You can also leverage libraries such as imbalanced-learn (imblearn) which offers easy-to-use pipelines for combining different resampling techniques.

Evaluating Hybrid Approaches

It is important to carefully evaluate the performance of models trained using hybrid approaches. Since multiple techniques are combined, there is a greater risk of overfitting or introducing unintended biases. Cross-validation and other rigorous evaluation strategies are crucial.

Pros and Cons of Hybrid Approaches

Pros

Can capitalize on the strengths of both data and algorithmic approaches.
Often results in a more balanced performance across classes.
Provides a wide array of combinations for experimentation.

Cons

Can be computationally more expensive.
The complexity of models can increase, making them harder to interpret and fine-tune.
Risk of overfitting or underfitting if not properly validated and tuned.