C4.5: programs for machine learning
C4.5: programs for machine learning
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
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
Mining needle in a haystack: classifying rare classes via two-phase rule induction
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Machine Learning
Visual Data Mining: Techniques and Tools for Data Visualization and Mining
Visual Data Mining: Techniques and Tools for Data Visualization and Mining
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Applications of Data Mining to Electronic Commerce
Data Mining and Knowledge Discovery
An Instance-Weighting Method to Induce Cost-Sensitive Trees
IEEE Transactions on Knowledge and Data Engineering
Knowledge discovery from data?
IEEE Intelligent Systems
AdaCost: Misclassification Cost-Sensitive Boosting
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Concept-Learning in the Presence of Between-Class and Within-Class Imbalances
AI '01 Proceedings of the 14th Biennial Conference of the Canadian Society on Computational Studies of Intelligence: Advances in Artificial Intelligence
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Cost-Sensitive Learning by Cost-Proportionate Example Weighting
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Data mining in metric space: an empirical analysis of supervised learning performance criteria
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Decision trees with minimal costs
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An empirical comparison of supervised learning algorithms
ICML '06 Proceedings of the 23rd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Sensitivity Analysis for Selective Learning by Feedforward Neural Networks
Fundamenta Informaticae
A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
The Journal of Machine Learning Research
Learning when training data are costly: the effect of class distribution on tree induction
Journal of Artificial Intelligence Research
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Data Mining for Business Applications: Introduction
Proceedings of the 2010 conference on Data Mining for Business Applications
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A central need in the emerging business of model-based prediction is to enable customers to validate the accuracy of a predictive product. This paper discusses how analysts can evaluate data mining models and their inferences from the customer viewpoint, where the customer is not particularly knowledgeable in data mining. To date, academia has focused primarily on the validation of algorithms through mathematical metrics and benchmarking studies. This type of validation is not sufficient in the business context, where organizations must validate specific models in terms that customers can understand quickly and effortlessly. We describe our predictive business and our customer validation needs. To that end, we discuss examples of customer needs, review issues associated with model validation, and point out how academic research may help to address these business needs.