Data Mining

Application of Synthetic Minority Over-sampling Technique (SMOTe) for Imbalanced Datasets

In Data Science, imbalanced datasets are no surprises. If the datasets intended for classification problems like Sentiment Analysis, Medical Imaging or other problems related to Discrete Predictive Analytics (for example-Flight Delay Prediction) have an unequal number of instances (samples or data points) for different classes, then those datasets are said to be imbalanced. This means that there is an imbalance between the classes in the dataset due to a large difference between the number of instances belonging to each class. The class having comparatively fewer instances than the other is known to be a minority with respect to the class having a comparatively larger number of the samples (known as a majority). An example of an imbalanced dataset is given below:

Here, there are 2 classes with labels: 0 and 1 with Gross Imbalance

Training a Machine Learning Model with this imbalanced dataset, often causes the model to develop a certain bias towards the majority class.

To tackle the issue of class imbalance, Synthetic Minority Over-sampling Technique (SMOTe) was introduced by Chawla et al. [3] in 2002.