Imbalanced classes in a dataset are often usual among classification problems in machine learning. Balancing an imbalanced class is crucial as the classification model, which is trained using the imbalanced class dataset will tend to exhibit the prediction accuracy according to the highest class of the dataset. Researchers have proposed several approaches to deal with this problem as well as improve the quality of the classifiers.
Below here, we listed down the top eight ways you can manage the imbalanced classes in your dataset.
1| Changing Performance Metric
Performance metrics play a fundamental role while building a machine learning model. Implying the incorrect performance metric on an imbalance dataset can yield wrong outcomes. For instance, although accuracy is considered to be an important metric for evaluating the performance of a machine learning model, sometimes it can be misleading in case of an imbalanced dataset. In such circumstances, one must use other performance metrics such as Recall, F1-Score, False Positive Rate (FPR), Precision, Area Under ROC Curve (AUROC), among others.
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2| The More Data, The Better
Machine learning models are data-hungry. In most cases, researchers spend most of their time in tasks like data cleaning, analysing, visualising, among others during an end-to-end machine learning process and contribute less time in data collection. While all of these steps are important, often the collection of data gets limited to certain numbers. To avoid such circumstances, one must add more data into the dataset. Collecting more data with relevant examples of the undersampled class of the dataset will help to overcome the issue.
3| Experiment With Different Algorithms
Another way to handle and manage imbalanced dataset is to try different algorithms rather than sticking to one particular algorithm. Experimenting with different algorithms provides a probability to check how the algorithms are performing by a particular dataset.
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4| Resampling of Dataset
To deal with the imbalanced dataset, researchers have introduced a number of resampling techniques. One of the benefits of using these techniques is that they are external approaches using the existing algorithms. They can be easily transportable in case of both undersampling and oversampling.
Some of the popular resampling methods are as follows-
- Random Oversampling: Oversampling seeks to increase the number of minority class members in the training set. Random oversampling is a simple approach to resampling, where one chooses members from the minority class at random. Then, these randomly chosen members are duplicated and added to the new training set.
- Random Undersampling: Undersampling is a process that seeks to reduce the number of majority class members in the training set. Random undersampling is a popular technique for resampling, where the majority class documents in the training set are randomly eliminated until the ratio between the minority and majority class is at the desired level.
5| Use of Ensemble Methods
Use of ensemble methods is one of the ways to handle the class imbalance problems of the dataset. The learning algorithms construct a set of classifiers and then classify new data points by making a choice of their predictions known as Ensemble methods. It has been discovered that ensembles are often much more accurate than the individual classifiers which make them up. Some of the commonly used Ensemble techniques are Bagging or Bootstrap Aggregation, Boosting and Stacking.
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6| Generating Synthetic Samples
Synthetic Minority Oversampling TEchnique or SMOTE is one of the most popular approaches for generating synthetic samples. SMOTE is an over-sampling approach in which the minority class is over-sampled by creating synthetic examples rather than by over-sampling with replacement.
It is basically a combination of oversampling the minority (abnormal) class and undersampling the majority (normal) class, which is found to achieve better classifier performance (in ROC space) than only undersampling the majority class. In this technique, synthetic examples are generated in a less application-specific manner, by operating in the feature space rather than data space.
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7| Multiple Classification System
Multiple Classification system is an approach where a classification system is built for imbalanced data based on the combination of several classifiers. It is a method for building multiple classifier systems in which each constituting classifier is trained on a subset of the majority class and on the whole minority class. The basis behind this method is the partition of the set of samples of the majority class in several subsets, each consisting of as many samples as the minority class.
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8| Use of Cost-Sensitive Algorithms
Cost-Sensitive Learning is a type of learning that takes the misclassification or other types of costs into consideration. Cost-sensitive learning is a popular and common approach to solve the class imbalanced datasets. Popular machine learning libraries such as support vector machines (SVM), random forest, decision trees, logistic regression, among others, can be configured using the cost-sensitive training.
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