The nature of statistical learning theory
The nature of statistical learning theory
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
Combination of multiple classifiers for the customer's purchase behavior prediction
Decision Support Systems - Special issue: Agents and e-commerce business models
Predicting Customer Behavior in Telecommunications
IEEE Intelligent Systems
Expert Systems with Applications: An International Journal
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
Cost-Sensitive-Data Preprocessing for Mining Customer Relationship Management Databases
IEEE Intelligent Systems
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Artificial Intelligence in Medicine
Toward a hybrid data mining model for customer retention
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Customer churn prediction by hybrid neural networks
Expert Systems with Applications: An International Journal
Mining changes in customer behavior in retail marketing
Expert Systems with Applications: An International Journal
In-depth behavior understanding and use: The behavior informatics approach
Information Sciences: an International Journal
Consensus-Based Distributed Support Vector Machines
The Journal of Machine Learning Research
Boosting support vector machines for imbalanced data sets
Knowledge and Information Systems
Building comprehensible customer churn prediction models with advanced rule induction techniques
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Data augmentation by predicting spending pleasure using commercially available external data
Journal of Intelligent Information Systems
Journal of Intelligent Information Systems
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
CLAP: Collaborative pattern mining for distributed information systems
Decision Support Systems
Foundations and Trends® in Machine Learning
A survey of the state of the art in learning the kernels
Knowledge and Information Systems
Hi-index | 0.00 |
In the customer-centered marketplace, the understanding of customer behavior is a critical success factor. The big databases in an organization usually involve multiplex data such as static, time series, symbolic sequential and textual data which are separately stored in different databases of different sections. It poses a challenge to traditional centralized customer behavior prediction. In this study, a novel approach called collaborative multiple kernel support vector machine (C-MK-SVM) is developed for distributed customer behavior prediction using multiplex data. The alternating direction method of multipliers (ADMM) is used for the global optimization of the distributed sub-models in C-MK-SVM. Computational experiments on a practical retail dataset are reported. Computational results show that C-MK-SVM exhibits better customer behavior prediction performance and higher computational speed than support vector machine and multiple kernel support vector machine.