Learning Binary Relations Using Weighted Majority Voting
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Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
Data Mining and Knowledge Discovery
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Combination of multiple classifiers for the customer's purchase behavior prediction
Decision Support Systems - Special issue: Agents and e-commerce business models
Pruning Decision Trees with Misclassification Costs
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Genetic Modelling of Customer Retention
EuroGP '98 Proceedings of the First European Workshop on Genetic Programming
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Multiclassifier Systems: Back to the Future
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Credit rating analysis with support vector machines and neural networks: a market comparative study
Decision Support Systems - Special issue: Data mining for financial decision making
Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalance Problem
IEEE Transactions on Knowledge and Data Engineering
Expert Systems with Applications: An International Journal
Advanced Engineering Informatics
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
ADCOM '07 Proceedings of the 15th International Conference on Advanced Computing and Communications
Expert Systems with Applications: An International Journal
From dynamic classifier selection to dynamic ensemble selection
Pattern Recognition
Expert Systems with Applications: An International Journal
A comparative study on rough set based class imbalance learning
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Customer churn prediction using improved balanced random forests
Expert Systems with Applications: An International Journal
Dynamic Classifier Ensemble Selection Based on GMDH
CSO '09 Proceedings of the 2009 International Joint Conference on Computational Sciences and Optimization - Volume 01
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
A data driven ensemble classifier for credit scoring analysis
Expert Systems with Applications: An International Journal
Least squares support vector machines ensemble models for credit scoring
Expert Systems with Applications: An International Journal
Multiple classifier application to credit risk assessment
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Ensemble strategies with adaptive evolutionary programming
Information Sciences: an International Journal
No free lunch and free leftovers theorems for multiobjective optimisation problems
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
A dynamic classifier ensemble selection approach for noise data
Information Sciences: an International Journal
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Hi-index | 12.05 |
Customer classification is widely used in customer relationship management including churn prediction, credit scoring, cross-selling and so on. In customer classification, an important yet challenging problem is the imbalance of data distribution. In this paper, we combine ensemble learning with cost-sensitive learning, and propose a dynamic classifier ensemble method for imbalanced data (DCEID). For each test customer, it can adaptively select out the more appropriate one from the two kinds of dynamic ensemble approach: dynamic classifier selection (DCS) and dynamic ensemble selection (DES). Meanwhile, new cost-sensitive selection criteria for DCS and DES are constructed respectively to improve the classification ability for imbalanced data. We apply this method to a credit scoring dataset in UCI and a real churn prediction dataset from a telecommunication company. The experimental results show that the classification performance of DCEID is not only better than some static ensemble methods such as weighted random forests and improved balanced random forests, but also better than the existing DCS and DES strategies.