Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Data preparation for data mining
Data preparation for data mining
The New Science of Management Decision
The New Science of Management Decision
Business applications of data mining
Communications of the ACM - Evolving data mining into solutions for insights
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Options in the Real World: Lessons Learned in Evaluating Oil and Gas Investments
Operations Research
Sequential cost-sensitive decision making with reinforcement learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Tree Induction for Probability-Based Ranking
Machine Learning
Economical active feature-value acquisition through Expected Utility estimation
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
Evaluating strategic options using decision-theoretic planning
Information Technology and Management
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Guest editorial: special issue on utility-based data mining
Data Mining and Knowledge Discovery
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The objective of this paper is to use a challenging real-world problem to illustrate how a probabilistic predictive model can provide the foundation for decision-analytic feedforward control. Commercial data mining software and sales data from a market research firm are used to create a predictive model of market success in the video game industry. A procedure is then described for transforming the classification trees into a decision-analytic model that can be solved to produce a value-maximizing game development policy. The video game example shows how the compact predictive models created by data mining algorithms can help to make decision-analytic feedforward control feasible, even for large, complex problems. However, the example also highlights the bounds placed on the practicality of the approach due to combinatorial explosions in the number of contingencies that have to be modeled. We show, for example, how the "option value" of sequels creates complexity that is effectively impossible to address using conventional decision analysis tools.