Communications of the ACM
The nature of statistical learning theory
The nature of statistical learning theory
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Real time facial expression recognition in video using support vector machines
Proceedings of the 5th international conference on Multimodal interfaces
Rule extraction from linear support vector machines
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Fuzzy Rule Extraction from Support Vector Machines
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Rule Extraction from Support Vector Machines: A Sequential Covering Approach
IEEE Transactions on Knowledge and Data Engineering
Artificial Intelligence in Medicine
Toward a hybrid data mining model for customer retention
Knowledge-Based Systems
Predicting credit card customer churn in banks using data mining
International Journal of Data Analysis Techniques and Strategies
Decompositional Rule Extraction from Support Vector Machines by Active Learning
IEEE Transactions on Knowledge and Data Engineering
Expert Systems with Applications: An International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Rule extraction from trained support vector machines
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
A novel evolutionary data mining algorithm with applications to churn prediction
IEEE Transactions on Evolutionary Computation
Data mining via rules extracted from GMDH: an application to predict churn in bank credit cards
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Preprocessing unbalanced data using support vector machine
Decision Support Systems
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In this work, an eclectic procedure for rule extraction from Support Vector Machine is proposed, where Tree is generated using Naïve Bayes Tree (NBTree) resulting in the SVM+NBTree hybrid. The data set analyzed in this paper is about churn prediction in bank credit cards and is obtained from Business Intelligence Cup 2004. The data set under consideration is highly unbalanced with 93.11% loyal and 6.89% churned customers. Since identifying churner is of paramount importance from business perspective, sensitivity of classification model is more critical. Using the available, original unbalanced data only, we observed that the proposed hybrid SVM+NBTree yielded the best sensitivity compared to other classifiers.