Linguistic Refinement of Temporal Rules

Thomas Sudkamp

doi:10.1109/NAFIPS.2006.365490

Linguistic Refinement of Temporal Rules

Thomas Sudkamp

Computer Science and Engineering

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

Abstract

The objective of hypothesis refinement is to modify the scope of a rule to more accurately model the data. In this paper we examine the relation between data summarization and hypothesis refinement in association rules with fuzzy temporal constraints. We then present two refinement strategies based on disjunctive constraint generalization and constraint specialization. Disjunctive generalization produces more general rules by merging adjacent constraints in the partition of the window of relevance. Temporal specification uses linguistic hedges to reduce the constraint window while maintaining the interpretability of the rule. The refinement strategies are developed to maintain or enhance the linguistic interpretability of the rules.

Original language	American English
Title of host publication	NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society
Publisher	IEEE
Pages	673-678
Number of pages	6
ISBN (Print)	1-4244-0362-6, 1-4244-0363-4
DOIs	https://doi.org/10.1109/NAFIPS.2006.365490
State	Published - 2006
Event	NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society - Montreal, QC, Canada Duration: Jun 3 2006 → Jun 6 2006

Conference

Conference	NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society
Country/Territory	Canada
City	Montreal, QC
Period	6/3/06 → 6/6/06

ASJC Scopus Subject Areas

General Computer Science
General Mathematics

Keywords

Association rules
Computer science
Decision making
Fuzzy sets
Humans
Machine learning
Merging
Statistical analysis

Disciplines

Computer Sciences
Engineering
Mathematics

Access to Document

10.1109/NAFIPS.2006.365490License: Unspecified

Cite this

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title = "Linguistic Refinement of Temporal Rules",

abstract = " The objective of hypothesis refinement is to modify the scope of a rule to more accurately model the data. In this paper we examine the relation between data summarization and hypothesis refinement in association rules with fuzzy temporal constraints. We then present two refinement strategies based on disjunctive constraint generalization and constraint specialization. Disjunctive generalization produces more general rules by merging adjacent constraints in the partition of the window of relevance. Temporal specification uses linguistic hedges to reduce the constraint window while maintaining the interpretability of the rule. The refinement strategies are developed to maintain or enhance the linguistic interpretability of the rules.",

keywords = "Association rules, Computer science, Decision making, Fuzzy sets, Humans, Machine learning, Merging, Statistical analysis",

author = "Thomas Sudkamp",

year = "2006",

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language = "American English",

isbn = "1-4244-0362-6",

pages = "673--678",

booktitle = "NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society",

publisher = "IEEE",

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PY - 2006

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N2 - The objective of hypothesis refinement is to modify the scope of a rule to more accurately model the data. In this paper we examine the relation between data summarization and hypothesis refinement in association rules with fuzzy temporal constraints. We then present two refinement strategies based on disjunctive constraint generalization and constraint specialization. Disjunctive generalization produces more general rules by merging adjacent constraints in the partition of the window of relevance. Temporal specification uses linguistic hedges to reduce the constraint window while maintaining the interpretability of the rule. The refinement strategies are developed to maintain or enhance the linguistic interpretability of the rules.

AB - The objective of hypothesis refinement is to modify the scope of a rule to more accurately model the data. In this paper we examine the relation between data summarization and hypothesis refinement in association rules with fuzzy temporal constraints. We then present two refinement strategies based on disjunctive constraint generalization and constraint specialization. Disjunctive generalization produces more general rules by merging adjacent constraints in the partition of the window of relevance. Temporal specification uses linguistic hedges to reduce the constraint window while maintaining the interpretability of the rule. The refinement strategies are developed to maintain or enhance the linguistic interpretability of the rules.

KW - Association rules

KW - Computer science

KW - Decision making

KW - Fuzzy sets

KW - Humans

KW - Machine learning

KW - Merging

KW - Statistical analysis

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UR - http://www.scopus.com/inward/citedby.url?scp=46749083409&partnerID=8YFLogxK

UR - https://corescholar.libraries.wright.edu/cse/393

U2 - 10.1109/NAFIPS.2006.365490

DO - 10.1109/NAFIPS.2006.365490

M3 - Conference contribution

SN - 1-4244-0362-6

SN - 1-4244-0363-4

SP - 673

EP - 678

BT - NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society

PB - IEEE

T2 - NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society

Y2 - 3 June 2006 through 6 June 2006

ER -

Linguistic Refinement of Temporal Rules

Abstract

Conference

ASJC Scopus Subject Areas

Keywords

Disciplines

Access to Document

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