An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models-Reference-Cited by-同舟云学术

An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models

Published:2011-04 Issue:1 Volume:51 Page:141-154
ISSN:0167-9236
Container-title:Decision Support Systems
language:en
Short-container-title:Decision Support Systems

Author:

Huysmans Johan,Dejaeger Karel,Mues Christophe,Vanthienen Jan,Baesens Bart

Publisher

Elsevier BV

Subject

Information Systems and Management,Arts and Humanities (miscellaneous),Developmental and Educational Psychology,Information Systems,Management Information Systems

Reference63 articles.

1. Survey and critique of techniques for extracting rules from trained artificial neural networks;Andrews;Knowledge Based Systems,1995

2. Benchmarking state of the art classification algorithms for credit scoring;Baesens;Journal of the Operational Research Society,2003

3. Behavioral aspects of information processing for the design of management information systems;Benbasat;IEEE Transactions on Systems, Man, and Cybernetics,1982

4. An experimental program investigating color-enhanced and graphical information presentation: An integration of the findings;Benbasat;Communications of the ACM,1986

5. Classification and Regression Trees;Breiman,1984

Cited by 226 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Designing explainable AI to improve human-AI team performance: A medical stakeholder-driven scoping review;Artificial Intelligence in Medicine;2024-03

2. Mitigating Algorithm Aversion in Recruiting: A Study on Explainable AI for Conversational Agents;ACM SIGMIS Database: the DATABASE for Advances in Information Systems;2024-02-06

3. An analysis of the ingredients for learning interpretable symbolic regression models with human-in-the-loop and genetic programming;ACM Transactions on Evolutionary Learning and Optimization;2024-01-30

4. On the evaluation of the symbolic knowledge extracted from black boxes;AI and Ethics;2024-01-09

5. Cost-Sensitive Trees for Interpretable Reinforcement Learning;Proceedings of the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD);2024-01-04