A reference model for customer-centric data mining with support vector machines

DOI10.1016/j.ejor.2008.12.017zbMath1176.90340OpenAlexW1974013939MaRDI QIDQ1042173

Stefan Voß, Stefan Lessmann

Publication date: 7 December 2009

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2008.12.017

zbMATH Keywords

marketingdata miningsupport vector machinescustomer relationship management

Mathematics Subject Classification ID

Marketing, advertising (90B60)

Related Items (9)

Individual-level social influence identification in social media: a learning-simulation coordinated method ⋮ Behavior-aware user response modeling in social media: learning from diverse heterogeneous data ⋮ A multi-kernel support tensor machine for classification with multitype multiway data and an application to cross-selling recommendations ⋮ Variable selection in classification model via quadratic programming ⋮ Functional-bandwidth kernel for support vector machine with functional data: an alternating optimization algorithm ⋮ How training on multiple time slices improves performance in churn prediction ⋮ A hierarchical multiple kernel support vector machine for customer churn prediction using longitudinal behavioral data ⋮ A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees ⋮ Assessing the impact of derived behavior information on customer attrition in the financial service industry

Uses Software

• UCI-ml
• LIBSVM
• AdaBoost.MH
• ElemStatLearn

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Benchmarking least squares support vector machine classifiers
• Customer base analysis: partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting
• Stochastic dominance-based rough set model for ordinal classification
• Modeling churn using customer lifetime value
• Wrappers for feature subset selection
• A decision-theoretic generalization of on-line learning and an application to boosting
• Tree induction for probability-based ranking
• Bayesian neural network learning for repeat purchase modelling in direct marketing
• Rough sets methodology for sorting problems in presence of multiple attributes and criteria
• Customer attrition analysis for financial services using proportional hazard models
• Quantitative models for direct marketing: A review from systems perspective
• Strategies for detecting fraudulent claims in the automobile insurance industry
• Optimal threshold analysis of segmentation methods for identifying target customers
• Operations research and data mining
• Bayesian kernel based classification for financial distress detection
• The impact of preprocessing on data mining: an evaluation of classifier sensitivity in direct marketing
• Recent developments in consumer credit risk assessment
• Comprehensible credit scoring models using rule extraction from support vector machines
• Using Neural Network Rule Extraction and Decision Tables for Credit-Risk Evaluation
• Credit Scoring and Its Applications
• Benchmarking state-of-the-art classification algorithms for credit scoring
• Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
• 10.1162/153244303322753616
• Diagrammatic Representation and Inference
• Gene selection for cancer classification using support vector machines

This page was built for publication: A reference model for customer-centric data mining with support vector machines