Foundations of data imbalance and solutions for a data democracy

Dealing with imbalanced data is a prevalent problem while performing classification on the datasets. Many times, this problem contributes to bias while making decisions or implementing policies. Thus, it is vital to understand the factors which causes imbalance in the data (or class imbalance). Such hidden biases and imbalances can lead to data tyranny, and a major challenge to a data democracy. In this chapter, two essential statistical elements are resolved: the degree of class imbalance and the complexity of the concept, solving such issues helps in building the foundations of a data democracy. Further, statistical measures which are appropriate in these scenarios are discussed and implemented on a real-life dataset (car insurance claims). In the end, popular data-level methods such as Random Oversampling, Random Undersampling, SMOTE, Tomek Link, and others are implemented in Python, and their performance is compared. Keywords - Imbalanced Data, Degree of Class Imbalance, Complexity of the Concept, Statistical Assessment Metrics, Undersampling and Oversampling 1. Motivation & Introduction In the real-world, data are collected from various sources like social networks, websites, logs, and databases. Whilst dealing with data from different sources, it is very crucial to check the quality of the data [1]. Data with questionable quality can introduce different types of biases in various stages of the data science lifecycle. These biases sometime can affect the association between variables, and in many cases could represent the opposite of the actual behavior [2].