Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Sampling-Based Stack Framework for Imbalanced Learning in Churn Prediction Subject The topic of the resource Churn prediction; ensemble classifiers; ensemble stack; over-sampling; under-sampling Description An account of the resource Churn prediction is gaining popularity in the research community as a powerful paradigm that supports data-driven operational decisions. Datasets related to churn prediction are often skewed with imbalanced class distribution. Data-level solutions, like over-sampling and under-sampling, have been commonly used by researchers to address this problem. There are limited number of case studies that attempt to evolve these data-level solutions by integrating them with computationally advanced frameworks, like ensembles. Ensembles primarily employ algorithmic diversity using a fixed set of training instances to achieve superior performance. This study aims to introduce algorithmic diversity in ensembles by modifying the fixed set of training instances using diverse sampling strategies to increase predictive performance in imbalanced learning. Data is acquired from the world's largest open hotel commerce platform company. A four-part series of experiments is conducted to analyze the effectiveness of sampling techniques and ensemble solutions on model performance. A new sampling-based stack framework called 'Stacking of Samplers for Imbalanced Learning' is proposed. The framework combines the prediction capabilities of sampling solutions to stimulate the information gain of the meta features in ensemble. It is observed that the proposed framework leads to improvement in model performance with AUC of 86.4% and top-decile lift of 4.7 for customers of the hotel technology provider. Additionally, results show that the framework records a higher information gain for meta features used in a stack, compared to commonly used stack frameworks. 2013 IEEE. Creator An entity primarily responsible for making the resource De S.; Prabu P. Source A related resource from which the described resource is derived IEEE Access, Vol-10, pp. 68017-68028. Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2022-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ACCESS.2022.3185227" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ACCESS.2022.3185227</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133674427&amp;doi=10.1109%2FACCESS.2022.3185227&amp;partnerID=40&amp;md5=8b07a43d126087ba6ccedc68eb3a7c0a" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133674427&amp;doi=10.1109%2fACCESS.2022.3185227&amp;partnerID=40&amp;md5=8b07a43d126087ba6ccedc68eb3a7c0a</a> Rights Information about rights held in and over the resource All Open Access; Gold Open Access Relation A related resource ISSN: 21693536 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant De S., CHRIST (Deemed to Be University), Department of Data Science, Bengaluru, 560029, India; Prabu P., CHRIST (Deemed to Be University), Department of Computer Science, Bengaluru, 560029, India