In machine learning, imbalanced classes in a dataset are quite often common among classification problems. Trying to balance an imbalanced class is extremely important since the classification model, which is trained using the imbalanced class dataset, manages to display predictive accuracy as per the highest class of the dataset.
The following enlisted below are the ways to manage imbalanced classes in your dataset:
Alter the performance metrics
Performance metrics play an integral role in constructing a model of machine learning. Indicating the inaccurate performance metric on a data set with an imbalance can yield false results. For example, while accuracy is regarded as an important metric for monitoring the effectiveness of a machine learning model, it can sometimes be completely inaccurate in the case of an imbalanced dataset. Other performance metrics such as Recall, F1-Score, False Positive Rate (FPR), Precision, Area Under ROC Curve (AUROC), among others, must be used in such circumstances.
A large amount of data is always better
Machine learning models are hungry for the data. In several instances, in an end-to-end machine learning process, researchers spend considerable time in tasks such as data cleaning, analyzing, visualizing, and contributing less time in data collection. Although each of these steps is essential, data collection is often limited to certain numbers. To avoid such situations, further data must be added to the dataset. Obtaining more data with appropriate concepts of the dataset’s under-sampled class will help resolve this issue.
Different algorithms to experiment
A further way of handling and managing an imbalanced dataset is to try different algorithms instead of sticking to one algorithm in particular. Experimenting with different algorithms gives the probability that a particular dataset will check how the algorithms are performing.
Researchers have developed several resampling strategies to tackle the imbalanced dataset. One of the advantages of using such techniques is that they are external approaches using the existing algorithms. In the case of both under-sampling and oversampling they can be easily transportable.
The ensemble method is one way of dealing with the dataset’s class imbalance issues. The learning algorithms build a set of classifiers and then categorize new data points by choosing their predictions, called Ensemble methods. Ensembles have often been discovered to be much more accurate than the individual classifiers which comprise them. Some of the frequently used Ensemble techniques are Aggregation, Boosting, Stacking, Bagging, or Bootstrap.
Generation of Synthetic Sample
Synthetic Minority Oversampling Technique or SMOTE is among the most popular methods for synthetic sample generation. SMOTE is an over-sampling method that over-samples the minority class by creating synthetic examples rather than over-sampling with substitution. It is a combination of minority (abnormal) class over-sampling and under-sampling of the majority (normal) class, which is found to achieve better classification performance (in ROC space) than just under-sampling the majority class.
With the above-mentioned ways, anybody can manage their imbalance classes of datasets without wasting much time.