An inductive inference model to elicit noncompensatory judgment strategies

Jing Yin; Ling Rothrock

doi:10.1007/978-3-642-21602-2_45

An inductive inference model to elicit noncompensatory judgment strategies

Jing Yin, Ling Rothrock

Marcus Department of Industrial and Manufacturing Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The proposed research developed a noncompensatory policy capturing technique to infer judgment rules (represented in disjunctive normal form) from available human data. The rule induction algorithm employs multiobjective Genetic Algorithm (GA) as its central search mechanism to enhance the induction and classification process. The quality of the induced rule set is measured by two criteria, fidelity (the degree to which the rule set reflects the judgment data they have been extracted from) and compactness (the simplicity of the rule set). An experimental study is conducted to demonstrate the effectiveness of the algorithm on a number of benchmark datasets.

Original language	English (US)
Title of host publication	Human-Computer Interaction
Subtitle of host publication	Design and Development Approaches - 14th International Conference, HCI International 2011, Proceedings
Pages	414-422
Number of pages	9
Edition	PART 1
DOIs	https://doi.org/10.1007/978-3-642-21602-2_45
State	Published - 2011
Event	14th International Conference on Human-Computer Interaction, HCI International 2011 - Orlando, FL, United States Duration: Jul 9 2011 → Jul 14 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 1
Volume	6761 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	14th International Conference on Human-Computer Interaction, HCI International 2011
Country/Territory	United States
City	Orlando, FL
Period	7/9/11 → 7/14/11

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-21602-2_45

Cite this

Yin, J., & Rothrock, L. (2011). An inductive inference model to elicit noncompensatory judgment strategies. In Human-Computer Interaction: Design and Development Approaches - 14th International Conference, HCI International 2011, Proceedings (PART 1 ed., pp. 414-422). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6761 LNCS, No. PART 1). https://doi.org/10.1007/978-3-642-21602-2_45

Yin, Jing ; Rothrock, Ling. / An inductive inference model to elicit noncompensatory judgment strategies. Human-Computer Interaction: Design and Development Approaches - 14th International Conference, HCI International 2011, Proceedings. PART 1. ed. 2011. pp. 414-422 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1).

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abstract = "The proposed research developed a noncompensatory policy capturing technique to infer judgment rules (represented in disjunctive normal form) from available human data. The rule induction algorithm employs multiobjective Genetic Algorithm (GA) as its central search mechanism to enhance the induction and classification process. The quality of the induced rule set is measured by two criteria, fidelity (the degree to which the rule set reflects the judgment data they have been extracted from) and compactness (the simplicity of the rule set). An experimental study is conducted to demonstrate the effectiveness of the algorithm on a number of benchmark datasets.",

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Yin, J & Rothrock, L 2011, An inductive inference model to elicit noncompensatory judgment strategies. in Human-Computer Interaction: Design and Development Approaches - 14th International Conference, HCI International 2011, Proceedings. PART 1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 1, vol. 6761 LNCS, pp. 414-422, 14th International Conference on Human-Computer Interaction, HCI International 2011, Orlando, FL, United States, 7/9/11. https://doi.org/10.1007/978-3-642-21602-2_45

An inductive inference model to elicit noncompensatory judgment strategies. / Yin, Jing; Rothrock, Ling.
Human-Computer Interaction: Design and Development Approaches - 14th International Conference, HCI International 2011, Proceedings. PART 1. ed. 2011. p. 414-422 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6761 LNCS, No. PART 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - The proposed research developed a noncompensatory policy capturing technique to infer judgment rules (represented in disjunctive normal form) from available human data. The rule induction algorithm employs multiobjective Genetic Algorithm (GA) as its central search mechanism to enhance the induction and classification process. The quality of the induced rule set is measured by two criteria, fidelity (the degree to which the rule set reflects the judgment data they have been extracted from) and compactness (the simplicity of the rule set). An experimental study is conducted to demonstrate the effectiveness of the algorithm on a number of benchmark datasets.

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Yin J, Rothrock L. An inductive inference model to elicit noncompensatory judgment strategies. In Human-Computer Interaction: Design and Development Approaches - 14th International Conference, HCI International 2011, Proceedings. PART 1 ed. 2011. p. 414-422. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 1). doi: 10.1007/978-3-642-21602-2_45

An inductive inference model to elicit noncompensatory judgment strategies

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