C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Mailing decisions in the catalog sales industry
Management Science
Approximation algorithms for NP-hard problems
Approximation algorithms for NP-hard problems
MetaCost: a general method for making classifiers cost-sensitive
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine learning and data mining
Communications of the ACM
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning and making decisions when costs and probabilities are both unknown
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Building Data Mining Applications for CRM
Building Data Mining Applications for CRM
Ensembling neural networks: many could be better than all
Artificial Intelligence
Optimal Database Marketing: Strategy, Development, and Data Mining
Optimal Database Marketing: Strategy, Development, and Data Mining
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Visualization Techniques for Mining Large Databases: A Comparison
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sequential cost-sensitive decision making with reinforcement learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Empirical Comparison of Various Reinforcement Learning Strategies for Sequential Targeted Marketing
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Optimal Actions for Profitable CRM
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Postprocessing Decision Trees to Extract Actionable Knowledge
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Using AUC and Accuracy in Evaluating Learning Algorithms
IEEE Transactions on Knowledge and Data Engineering
Mining Customer Value: From Association Rules to Direct Marketing
Data Mining and Knowledge Discovery
Mining Patterns That Respond to Actions
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Mining Actionable Patterns by Role Models
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Crm automation
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Cost-Sensitive-Data Preprocessing for Mining Customer Relationship Management Databases
IEEE Intelligent Systems
Domain-Driven, Actionable Knowledge Discovery
IEEE Intelligent Systems
Domain driven data mining to improve promotional campaign ROI and select marketing channels
Proceedings of the 18th ACM conference on Information and knowledge management
Combined association rule mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Web user browse behavior characteristic analysis based on a BC tree
AMT'10 Proceedings of the 6th international conference on Active media technology
Efficient action extraction with many-to-many relationship between actions and features
LORI'11 Proceedings of the Third international conference on Logic, rationality, and interaction
Post mining of diversified multiple decision trees for actionable knowledge discovery
ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
Actionable knowledge discovery and delivery
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
AssocExplorer: an association rule visualization system for exploratory data analysis
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
An integrative framework for intelligent software project risk planning
Decision Support Systems
Causality-based cost-effective action mining
Intelligent Data Analysis
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Most data mining algorithms and tools stop at discovered customer models, producing distribution information on customer profiles. Such techniques, when applied to industrial problems such as customer relationship management (CRM), are useful in pointing out customers who are likely attritors and customers who are loyal, but they require human experts to postprocess the discovered knowledge manually. Most of the postprocessing techniques have been limited to producing visualization results and interestingness ranking, but they do not directly suggest actions that would lead to an increase in the objective function such as profit. In this paper, we present novel algorithms that suggest actions to change customers from an undesired status (such as attritors) to a desired one (such as loyal) while maximizing an objective function: the expected net profit. These algorithms can discover cost-effective actions to transform customers from undesirable classes to desirable ones. The approach we take integrates data mining and decision making tightly by formulating the decision making problems directly on top of the data mining results in a postprocessing step. To improve the effectiveness of the approach, we also present an ensemble of decision trees which is shown to be more robust when the training data changes. Empirical tests are conducted on both a realistic insurance application domain and UCI benchmark data.