Tuning Expert Systems for Cost-Sensitive Decisions
Joint Authors
Source
Advances in Artificial Intelligence
Issue
Vol. 2011, Issue 2011 (31 Dec. 2011), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2011-06-30
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Information Technology and Computer Science
Science
Abstract EN
There is currently a growing body of research examining the effects of the fusion of domain knowledge and data mining.
This paper examines the impact of such fusion in a novel way by applying validation techniques and training data to enhance the performance of knowledge-based expert systems.
We present an algorithm for tuning an expert system to minimize the expected misclassification cost.
The algorithm employs data reserved for training data mining models to determine the decision cutoff of the expert system, in terms of the certainty factor of a prediction, for optimal performance.
We evaluate the proposed algorithm and find that tuning the expert system results in significantly lower costs.
Our approach could be extended to enhance the performance of any intelligent or knowledge system that makes cost-sensitive business decisions.
American Psychological Association (APA)
Sinha, Atish P.& Zhao, Huimin. 2011. Tuning Expert Systems for Cost-Sensitive Decisions. Advances in Artificial Intelligence،Vol. 2011, no. 2011, pp.1-12.
https://search.emarefa.net/detail/BIM-483021
Modern Language Association (MLA)
Sinha, Atish P.& Zhao, Huimin. Tuning Expert Systems for Cost-Sensitive Decisions. Advances in Artificial Intelligence No. 2011 (2011), pp.1-12.
https://search.emarefa.net/detail/BIM-483021
American Medical Association (AMA)
Sinha, Atish P.& Zhao, Huimin. Tuning Expert Systems for Cost-Sensitive Decisions. Advances in Artificial Intelligence. 2011. Vol. 2011, no. 2011, pp.1-12.
https://search.emarefa.net/detail/BIM-483021
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-483021