Call for Papers: Special Issue on Profit-Driven Analytics.

Abstract

Big DataVol. 5, No. 1 AnnouncementsFree AccessCall for Papers: Special Issue on Profit-Driven AnalyticsGuest Editors, Bart Baesens, Wouter Verbeke, and Cristián BravoGuest EditorsSearch for more papers by this author, Bart BaesensSearch for more papers by this author, Wouter VerbekeSearch for more papers by this author, and Cristián BravoSearch for more papers by this authorPublished Online:1 Mar 2017https://doi.org/10.1089/big.2017.29015.cfpAboutSectionsPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail Deadline for Submission: September 15, 2017Please include the special issue title in your cover letter when submitting your manuscriptSpecial Issue Publication Date: March 2018Businesses are gathering an unprecedented amount of data to gain deeper insights into customer behavior and markets with the bottom line in mind. Popular analytical applications are: churn prediction, response modeling, credit risk modeling, sales forecasting and anomaly detection. Several analytical techniques have been developed to address such problems, where the focus has typically been on algorithmic complexity, statistical significance or detection power. However, to be successful from a business standpoint, analytical models need to do much more, namely, add business value, provide interpretability, enhance operational efficiency, and keep business compliant in following correct practices.The objective of this special issue is to publish high-quality papers that address the added value of an analytical model from a business perspective. The issue will focus on methods, measurement, and practices that demonstrate business value. In addition to the usual technical evaluation criteria such as mean squared error, cross-entropy error, R-squared, lift curves, AUC, p-values, etc., the methods should make the connection to business value through the top or bottom line. The resulting findings and insights should help to further catalyze the impact of big data & analytics in practical business applications.Topics of interest include, but are not limited to: • Profit driven model evaluation and implementation• Cost-sensitive learning for classification• Cost-sensitive learning for regression• Cost-sensitive learning for segmentation• Cost-sensitive forecasting• Uplift modeling• Customer Lifetime Value modeling• Economical aspects of analytical models: Return on Investment (ROI), Total Cost of Ownership (TCO), etc.• Business value of big data technologies and models• Applications in marketing analytics, risk analytics, insurance analytics, HR analytics, supply chain analytics, customer journey analytics, text analytics, process analytics, healthcare analytics, etc.Submitted papers must contain new, unpublished, original, and fundamental work relating to the Big Data journal's mission statement. Purely theoretical papers, simple surveys, incremental contributions, and/or journalistic descriptions are not encouraged. Similarly, purely algorithmic development without practical applications and/or solely benchmarking exercises using test bed data sets are not part of the intended focus. All submissions will be reviewed using rigorous scientific criteria focusing on novelty and business impact.If you have any questions, please contact Professor Bart Baesens (bart.baesens@kuleuven.be).Please submit your papers online to our web-based manuscript submission and peer-review at:www.liebertpub.com/manuscript/bigBig Data is a highly innovative, peer-reviewed journal, provides a unique forum for world-class research exploring the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data, including data science, big data infrastructure and analytics, and pervasive computing.Deadline for Submission: September 15, 2017Please include the special issue title in your cover letter when submitting your manuscriptAdvantages of publishing in Big Data include: • Fast and user-friendly electronic submission• Rapid, high-quality peer review• Maximum exposure: accessible in 170 countries worldwide• Open Access options availableBios:Professor Bart Baesens is a professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom). He has done extensive research on big data & analytics, customer relationship management, web analytics, fraud detection, and credit risk management. His findings have been published in leading international journals including Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, and the Journal of Machine Learning Research. He has authored several books including Credit Risk Management: Basic Concepts, Analytics in a Big Data World, and Fraud Analytics Using Descriptive, Predictive and Social Network Techniques. He teaches E-learning courses on Advanced Analytics in a Big Data World and Credit Risk Modeling. His research is summarized at www.dataminingapps.com. He also regularly tutors, advises and provides consulting support to international firms with respect to their big data, analytics and credit risk management strategy.Wouter Verbeke is assistant professor and head of the Data Analytics Lab at Vrije Universiteit Brussel (Belgium). His research is situated in the field of predictive analytics and network analytics with a focus on value centric evaluation and learning. His work is driven by real-life business problems that require a data driven solution including applications in marketing, finance, fraud and cybersecurity, mobility and human resources. Wouter teaches several courses on information systems and advanced modeling for decision making to business students, and he regularly tutors workshops on fraud analytics, credit risk modeling and customer analytics to business professionals. His work has been published in international scientific journals such as IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Software Engineering, European Journal of Operational Research, Journal of Forecasting, and Decision Support Systems. In 2014, he won the EURO award for best article published in the European Journal of Operational Research in the category Innovative Applications of O.R. He is author of the book Fraud Analytics Using Descriptive, Predictive and Social Network Techniques. His research and current projects are summarized at https://www.data-lab.be/Dr. Cristián Bravo is Lecturer (assistant professor) in Business Analytics at The University of Southampton. Previously he served as Instructor Professor at University of Talca, Chile; Research Fellow at KU Leuven, Belgium; Research Director at the Finance Centre, Universidad de Chile, and Head of Business Intelligence at one of the largest insurance companies in Chile. His research focuses on the development of an application of predictive, descriptive and prospective analytics to the problem of credit risk in micro, small and medium enterprises; covering diverse topics and methodologies, such as semi-supervised techniques, social networks analytics, fraud analytics, reject inference, and multiple modeling methodologies. His work has been published in well-known international journals, he has edited two special issues in business analytics in reputed scientific journals, and he regularly teaches courses in Credit Risk and Analytics in academia and for companies worldwide.FiguresReferencesRelatedDetailsCited byMulti-Parallel Adaptive Grasshopper Optimization Technique for Detecting Anonymous Attacks in Wireless Networks1 April 2021 | Wireless Personal Communications, Vol. 119, No. 3 Volume 5Issue 1Mar 2017 InformationCopyright 2017, Mary Ann Liebert, Inc.To cite this article:Guest Editors, Bart Baesens, Wouter Verbeke, and Cristián Bravo.Call for Papers: Special Issue on Profit-Driven Analytics.Big Data.Mar 2017.3-4.http://doi.org/10.1089/big.2017.29015.cfpPublished in Volume: 5 Issue 1: March 1, 2017Online Ahead of Print:February 24, 2017PDF download