Fundamenta Informaticae

2014

DOI: 10.3233/fi-2014-956

|View full text |Cite

|

Sign up to set email alerts

|

Relationships Between Length and Coverage of Decision Rules

¹

,

²

,

Mikhail Moshkov

³

et al.

Abstract: The paper describes a new tool for study relationships between length and coverage of exact decision rules. This tool is based on dynamic programming approach. We also present results of experiments with decision tables from UCI Machine Learning Repository.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

0

Mentioning

0

Contrasting

0

Year Published

2016

2016

2020

2020

Publication Types

Select...

Article3

Book2

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 10 publications

References 21 publications

Supporting

0

Mentioning

0

Contrasting

0

Order By: Relevance

Measuring Quality of Decision Rules Through Ranking of Conditional Attributes

¹

2016

Intelligent Decision Technologies 2016

View full text Add to dashboard Cite

No abstract

Measuring Quality of Decision Rules Through Ranking of Conditional Attributes

¹

2016

Intelligent Decision Technologies 2016

View full text Add to dashboard Cite

No abstract

Weighting and Pruning of Decision Rules by Attributes and Attribute Rankings

¹

2016

Communications in Computer and Information Science

View full text Add to dashboard Cite

Abstract. Pruning is a popular post-processing mechanism used in search for optimal solutions when there is insufficient domain knowledge to either limit learning data or govern induction in order to infer only the most interesting or important decision rules. Filtering of generated rules can be driven by various parameters, for example explicit rule characteristics. The paper presents research on pruning rule sets by two approaches involving attribute rankings, the first relaying on selection of rules referring to the highest ranking attributes, which is compared to weighting of rules by calculated quality measures dependent on weights coming from attribute rankings that results in rule ranking.

Attributes reductions of bipolar fuzzy relation decision systems

¹

,

²

,

³

2019

Neural Comput & Applic

View full text Add to dashboard Cite

No abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Product

Browser Extension Assistant by scite Citation Statement Search Reference Check Visualizations Dashboards Explore Journals Explore Organizations Explore Funders Embedding Badge Embedding Citation Search Pricing

Resources

Blog Help & FAQ Accessibility Statement API Terms For Universities & Governments For Researchers For Publishers For Corporate, Pharma & Enterprise Author Marketing Become an Affiliate Get an organization trial or quote scite Data & Services

About

News & Press Careers Read our Paper Coverage

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Copyright © 2024 scite LLC. All rights reserved.

Made with 💙 for researchers

Part of the Research Solutions Family.