Maximizing classifier utility when training data is costly
ACM SIGKDD Explorations Newsletter
Guest editorial: special issue on utility-based data mining
Data Mining and Knowledge Discovery
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Towards the Generic Framework for Utility Considerations in Data Mining Research
Proceedings of the 2010 conference on Data Mining for Business Applications
Towards cost-sensitive learning for real-world applications
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
| Hi-index | 0.00 |
Early work in predictive data mining and machine learning rarely addressed the complex circumstances in which knowledge is extracted and applied. It often assumed that training data were freely available and focused on simple objectives, namely predictive accuracy. Over time, there has been a growing interest in the machine learning and data mining communities in research addressing economical data acquisition, utility-based methods for knowledge induction and application and methodologies for evaluating the utility derived from data mining techniques.This workshop explores the notion of economic utility and how it can be maximized throughout the data mining process. As of today much of the work focuses on a single aspect data mining. The workshop aims to bring together researchers from data mining and machine learning to share their perspective on key challenges in utility-based data mining and how individual contributions made thus far can be combined towards a comprehensive utility-based methodology.We believe the very positive response we have had from both academia and industry indicates the importance of utility-based data mining research and hope that the workshop will promote a fruitful exchange of ideas to further advance the field.