In the machine learning scenario, the class imbalance problem stands as a forbidding obstacle to achieve accurate and unbiased results. Imbalance class occurs when the scattering of classes within a dataset is severely twisted with one class having large number of data as compared to the other. This consequence has a great set of problems specifically where the main interest of finding accuracy lies in minority class.
Traditional algorithms favor majority class to find accuracy, completely ignoring the outcomes of minority class leading to a biased result which is not favorable for real world applications. To eradicate this issue, we have different strategies such as oversampling and undersampling techniques to balance dataset for both the classes by either imitating dataset or by removing certain dataset. Both the techniques hamper the accuracy of results. Oversampling increases the number of datasets in the minor class by duplicating existing samples. Undersampling reduces the number of instances and selects efficient samples only from major class. But both the methods reduce the quality of the accuracy compromising on the final outcomes.Algorithms like SMOTE, ADASYN generate synthetic dataset for minority class, somewhat improving imbalance in classes.
Along with that we have potent technique-ensemble learning. It combines different basic models to predict outcomes. In respect of class imbalance these learning methods can guide by predicting biasing towards the majority class by training multiple model trained on subsets of the datasets. Boosting and bagging is such ensemble learning techniques. Boosting algorithms like AdaBoost, GBM et al. train weak learners focusing on each instances misclassified by the previous data. As it highlight learning from misclassified sets thus enlightening performance of imbalance class. Whereas, bagging trains various base models on arbitrary subsets of dataset. By taking average predictions made by these models it reduces the prediction and can be used on imbalanced datasets. Stacking combines the multiple base models. Stacking improves robustness of prediction by complementary strengths of various models.
One more refined method for addressing class imbalance is contextual learning. It involves combining contextual information into learning process to improve performance on minority class. By leveraging domain specific secondary data sources, it can improve representation of minor class and develop prediction of the model accurately.
Although, advancement in deep learning have shown potential result in handling class imbalance by elaborated representation of the data. Focal loss and class specific metrics enhance the model by encapsulating patterns from imbalanced datasets.Regardless of the work we have achieved so far the challenge remains the same for more efficient solutions.
In conclusion, class imbalance in machine learning is a vital step in real application. By using appropriate combination techniques and algorithms we may be able to capture patterns from class imbalanced dataset and have fair outcomes in various real life applications.As we traverse between the complexities of machine learning algorithms of imbalanced datasets, we may try to encircle the challenges we face and move towards a more promising results of machine learning algorithms.
By Shweta Chaudhary
