Applied multivariate statistical analysis
Applied multivariate statistical analysis
Computer systems that learn: classification and prediction methods from statistics, neural nets, machine learning, and expert systems
C4.5: programs for machine learning
C4.5: programs for machine learning
Case-based reasoning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Principles of data mining
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
The Role of Domain Knowledge in a Large Scale Data Mining Project
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Introduction to the special issue on the fusion of domain knowledge with data for decision support
The Journal of Machine Learning Research
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Journal of Management Information Systems - Special section: Data mining
Dynamics of modeling in data mining: interpretive approach to bankruptcy prediction
Journal of Management Information Systems - Special section: Data mining
Journal of Management Information Systems - Special section: Data mining
Performance evaluation of neural network decision models
Journal of Management Information Systems - Special section: Strategic and competitive information systems
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
A Case-Based Reasoning System for Indirect Bank Lending
International Journal of Intelligent Systems in Accounting and Finance Management
Metadata and its impact on libraries: Book Reviews
Journal of the American Society for Information Science and Technology
Instance weighting versus threshold adjusting for cost-sensitive classification
Knowledge and Information Systems
Incorporating domain knowledge into data mining classifiers: An application in indirect lending
Decision Support Systems
Expert Systems with Applications: An International Journal
Intrusion Prevention in Information Systems: Reactive and Proactive Responses
Journal of Management Information Systems
Tuning Data Mining Methods for Cost-Sensitive Regression: A Study in Loan Charge-Off Forecasting
Journal of Management Information Systems
Commercial Internet filters: Perils and opportunities
Decision Support Systems
When to choose an ensemble classifier model for data mining
International Journal of Business Intelligence and Data Mining
An extended tuning method for cost-sensitive regression and forecasting
Decision Support Systems
Tuning expert systems for cost-sensitive decisions
Advances in Artificial Intelligence
International Journal of Human-Computer Studies
Cost-Sensitive Learning via Priority Sampling to Improve the Return on Marketing and CRM Investment
Journal of Management Information Systems
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In this study, we conduct an empirical analysis of the performance of five popular data mining methods--neural networks, logistic regression, linear discriminant analysis, decision trees, and nearest neighbor--on two binary classification problems from the credit evaluation domain. Whereas most studies comparing data mining methods have employed accuracy as a performance measure, we argue that, for problems such as credit evaluation, the focus should be on minimizing misclassification cost. We first generate receiver operating characteristic (ROC) curves for the classifiers and use the area under the curve (AUC) measure to compare aggregate performance of the five methods over the spectrum of decision thresholds. Next, using the ROC results, we propose a method for tuning the classifiers by identifying optimal decision thresholds. We compare the methods based on expected costs across a range of cost-probability ratios. In addition to expected cost and AUC, we evaluate the models on the basis of their generalizability to unseen data, their scalability to other problems in the domain, and their robustness against changes in class distributions. We found that the performance of logistic regression and neural network models was superior under most conditions. In contrast, decision tree and nearest neighbor models yielded higher costs, and were much less generalizable and robust than the other models. An important finding of this research is that the models can be effectively tuned post hoc to make them cost sensitive, even though they were built without incorporating misclassification costs.