Handling Imbalanced and Overlapping Classes in Smart Environments Prompting Dataset

The area of supervised machine learning often encounters imbalanced class distribution problem where one class is under represented as compared to other classes. Additionally, in many real-life problem domains, data with an imbalanced class distribution contains ambiguous regions in the data space where the prior probability of two or more classes are approximately equal. This problem, known as overlapping classes, thus makes it difficult for the learners in classification task. In this chapter, intersection between the problems of imbalanced class and overlapping classes is explored from the perspective of Smart Environments as the application domain. In smart environments, the task of delivering in-home interventions to residents for timely reminders or brief instructions to ensure successful completion of daily activities, is an ideal scenario for the problem. As a solution to the aforementioned problem, a novel clustering-based under-sampling (ClusBUS) technique is proposed. Density-based clustering technique, DBSCAN, is used to identify “interesting” clusters in the instance space on which under-sampling is performed on the basis of a threshold value for degree of minority class dominance in the clusters.