We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities. While the prior and conditional probabilities in the model do not change from user to user, the utility models do. Thus it is necessary to elicit a utility model separately for each new user. Elicitation is long and tedious, particularly if the outcome space is large and not decomposable. There are two common approaches to utility function elicitation. The first is to base the determination of the users utility function solely ON elicitation OF qualitative preferences.The second makes assumptions about the form AND decomposability OF the utility function.Here we take a different approach: we attempt TO identify the new USERs utility function based on classification relative to a database of previously collected utility functions. We do this by identifying clusters of utility functions that minimize an appropriate distance measure. Having identified the clusters, we develop a classification scheme that requires many fewer and simpler assessments than full utility elicitation and is more robust than utility elicitation based solely on preferences. We have tested our algorithm on a small database of utility functions in a prenatal diagnosis domain and the results are quite promising.
Bayesian preference elicitation for decision support in multiobjective optimization
Institute of Applied Analysis（应用分析研究所）；Numerical Simulation , University of Stuttgart, Germany（数值模拟，斯图加特大学，德国）；Institute for Parallel（并行系统研究所）；Distributed Systems , University of Stuttgart, Germany（分布式系统，斯图加特大学，德国）；Surrogate Modeling Lab , Ghent University - imec, Belgium（替代建模实验室，根特大学-imec，比利时）；Department of Computing（计算部门）；Mathematical Sciences , California Institute of Technology, USA（数学科学，加州理工学院，美国）
Preference Elicitation for Policy Optimization and Application to Aligning Heart Transplantation with Human Values
Carnegie Mellon University（卡内基梅隆大学）；Medical University of South Carolina（南卡罗来纳医科大学）；Strategy Robot, Inc.（策略机器人公司）；Strategic Machine, Inc.（战略机器公司）；Optimized Markets, Inc.（优化市场公司）
Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making
Vrije Universiteit Brussel（布鲁塞尔自由大学）；University of Oxford（牛津大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；City of Amsterdam, Signal Control Design and Analysis（阿姆斯特丹市信号控制设计与分析部门）；Delft University of Technology（代尔夫特理工大学）
Cross-Fitted Residual Utility for Primary-Preserving Cognitive Decision Correction in Automatic Modulation Classification
School of Mathematical Sciences, University of Electronic Science and Technology of China（电子科技大学数学科学学院）；School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）
A robust hierarchical nominal classification method based on similarity and dissimilarity using loss function and an improved version of the deck of cards method
INESC-ID（葡萄牙国家工程与技术研究院信息与决策系统研究所）；University of Catania（卡塔尼亚大学）；University of Portsmouth（朴茨茅斯大学）
Smart System: Joint Utility and Frequency for Pattern Classification
Jinan University of Birmingham Joint Institute（暨南大学伯明翰大学联合学院）；Jinan University（暨南大学）；Guangdong University of Technology（广东工业大学）；Shandong University of Science and Technology（山东科技大学）
Enhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
Universitat de Barcelona（巴塞罗那大学）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）；Technical University of Munich（慕尼黑工业大学）；Imperial College London（伦敦帝国学院）；Computer Vision Center（计算机视觉中心）；Kings College London（伦敦国王学院）；Institució Catalana de Recerca i Estudis Avançats (ICREA)（加泰罗尼亚高等研究院（ICREA））
