Original Contribution: Stacked generalization
Neural Networks
Machine Learning
The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
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PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
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CAiSE'07 Proceedings of the 19th international conference on Advanced information systems engineering
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This paper has two main objectives. One is presenting a hybrid framework for KDD project development where all tools for KDD project development are integrated. The other is providing an integrated environment for knowledge reuse, for preventing recurrence of known errors, based on previous experience. Different from purely algorithmic papers, this one focuses on performance metrics used for managerial activities such as the time taken for solution development, the amount of files not automatically managed and others, while preserving equivalent performance on the technical solution. This framework has been validated with metadata collected from previous projects developed and deployed for real world applications by the development team members, including public data mining competitions. The case study carried out in actual contracted projects have shown that this framework assesses the risk of failure for new projects, controls and documents all the KDD project development process and helps understanding the conditions that lead KDD projects to success.