Proceedings of the 31st ACM International Conference on Information &Amp; Knowledge Management 2022
DOI: 10.1145/3511808.3557088
|View full text |Cite
|
Sign up to set email alerts
|

An Adaptive Framework for Confidence-constraint Rule Set Learning Algorithm in Large Dataset

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
2
0

Year Published

2023
2023
2025
2025

Publication Types

Select...
3
2

Relationship

0
5

Authors

Journals

citations
Cited by 7 publications
(2 citation statements)
references
References 21 publications
0
2
0
Order By: Relevance
“…One popular approach to interpretable models is the use of decision rule sets [2], [3], [4], [5], [6], which are inherently interpretable because the rules are expressed in simple if-then sentences that correspond to logical combinations of input conditions that must be satisfied for a classification. While decision rule sets are natural classifiers, for which the performance is generally measured by the overall classification accuracy, coverage and rule precision are also commonly considered important metrics of decision rules.…”
Section: A Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…One popular approach to interpretable models is the use of decision rule sets [2], [3], [4], [5], [6], which are inherently interpretable because the rules are expressed in simple if-then sentences that correspond to logical combinations of input conditions that must be satisfied for a classification. While decision rule sets are natural classifiers, for which the performance is generally measured by the overall classification accuracy, coverage and rule precision are also commonly considered important metrics of decision rules.…”
Section: A Related Workmentioning
confidence: 99%
“…While decision rule sets are natural classifiers, for which the performance is generally measured by the overall classification accuracy, coverage and rule precision are also commonly considered important metrics of decision rules. In particular, [6] proposes to impose an additional constraint on precision to improve the performance of the rule sets. Besides decision rule sets, decision lists [7], [8] and decision trees [9] are also interpretable rule-based models.…”
Section: A Related Workmentioning
confidence: 99%