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 Representation-Based Query Strategy to Derive Qualitative Features for Improved Churn Prediction Subject The topic of the resource Active learning; churn prediction; entropy; query strategy; topic classification Description An account of the resource The effectiveness of any Machine Learning process depends on the accuracy of annotated data that is used to train a learner. However, manual annotation is expensive. Hence, researchers adopt a semi-supervised approach called active learning that aims to achieve state-of-the-art performance using minimal number of samples. Although it boosts classifier performance, the underlying query strategies are unable to eliminate redundancy in selected samples. Redundant samples lead to increased cost and sub-optimal performance of learner. Inspired by this challenge, the study proposes a new representation-based query strategy that selects highly informative and representative subsets of samples for manual annotation. Data comprises messages of a set of customers sent to a service provider. Series of experiments are conducted to analyze the effectiveness of the proposed query strategy, called 'Entropy-based Min Max Similarity' (E-MMSIM), in the context of topic classification for churn prediction. The foundation of E-MMSIM is an algorithm that is popularly used to sequence proteins in protein databases. The algorithm is modified and utilized to select the most representative and informative samples. The performance is evaluated using F1-score, AUC and accuracy. It is observed that 'E-MMSIM' outperforms popular query strategies, and improves performance of topic classifiers for each of the 4 topics of churn prediction. The trained topic classifiers are used to derive qualitative features. These features are further integrated with structured variables for the same group of customers to predict churn. Experiments provide evidence that inclusion of qualitative features derived using E-MMSIM, enhance the performance of churn classifiers by 5%. 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-11, pp. 1213-1223. 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 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/ACCESS.2022.3233768" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/ACCESS.2022.3233768</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147220302&amp;doi=10.1109%2FACCESS.2022.3233768&amp;partnerID=40&amp;md5=fa0917a76096ca7cb58c24d1ee8ce02c" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85147220302&amp;doi=10.1109%2fACCESS.2022.3233768&amp;partnerID=40&amp;md5=fa0917a76096ca7cb58c24d1ee8ce02c</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