Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T12:42:51.403085Z
Paper Citation Record · LEDGER
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning
As of 16 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:1908.06874.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
14 of 14 outbound references displayed
- verified exact0
- verified fuzzy14
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 930de1e7-501a-4aa8-8336-b7131aebb0a8 · outbound
This paper cites Effective rule-based multi-label classification with learning classifier systems.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning Effective rule-based multi-label classification with learning classifier systems
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.632141Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 54890fc7-f21f-48ae-a686-187297c8ef6d · outbound
This paper cites An evolutionary multi label classification using associative rule mining for spatial preferences.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning An evolutionary multi label classification using associative rule mining for spatial preferences
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.617209Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation d29db1a2-b9f4-44a8-a352-60a52334b6bd · outbound
This paper cites Evolving multi-label clas- sification rules with gene expression programming: A preliminary study.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning Evolving multi-label clas- sification rules with gene expression programming: A preliminary study
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.602629Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 22506194-a706-421d-b0a4-45dd1276ab30 · outbound
This paper cites LI-MLC: A label inference methodology for addressing high dimensionality in the label space for multilabel classification.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning LI-MLC: A label inference methodology for addressing high dimensionality in the label space for multilabel classification
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.588545Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 00ea003c-2451-4111-a828-2322a83c89ed · outbound
This paper cites On label dependence and loss minimization in multi-label classification.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning On label dependence and loss minimization in multi-label classification
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.573946Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation bf4d03f9-6379-4a7d-bc64-912be2d4388a · outbound
This paper cites Interpretable decision sets: A joint framework for description and prediction.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning Interpretable decision sets: A joint framework for description and prediction
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.559705Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 044b55fc-1736-4f02-b23d-9e97c88e4ca8 · outbound
This paper cites Multi-label Classification based on Association Rules with Application to Scene Classification.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning Multi-label Classification based on Association Rules with Application to Scene Classification
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.545354Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 2c95a144-dfc8-4daa-8436-49a62d8f18e6 · outbound
This paper cites Learning interpretable rules for multi-label classification.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning Learning interpretable rules for multi-label classification
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.530755Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation f2dc6764-7d76-40a9-9be5-9b184a070d04 · outbound
This paper cites Learning rules for multi-label classification: A stacking and a separate-and-conquer approach.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning Learning rules for multi-label classification: A stacking and a separate-and-conquer approach
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.516006Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e9091d7e-f2b4-41ac-8238-bfa2aba2960c · outbound
This paper cites Discovering and exploiting deterministic label relationships in multi-label learning.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning Discovering and exploiting deterministic label relationships in multi-label learning
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.499806Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 14fade10-2c17-4121-8522-8ff833ff5a43 · outbound
This paper cites Multi-label classification with label constraints.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning Multi-label classification with label constraints
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.484513Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 57f80683-2273-44d1-8d57-a5dcafc1d305 · outbound
This paper cites Exploiting anti-monotonicity of multi-label evaluation measures for inducing multi-label rules.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning Exploiting anti-monotonicity of multi-label evaluation measures for inducing multi-label rules
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.469990Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 145cda0f-b960-4ab1-93c8-168ef96b67ee · outbound
This paper cites Multiple labels associative classifi- cation.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning Multiple labels associative classifi- cation
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.454096Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation acbb906e-db86-437a-8f4a-298f5306fd86 · outbound
This paper cites Mining multi-label data.
Efficient Discovery of Expressive Multi-label Rules using Relaxed Pruning Mining multi-label data
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:42:51.438130Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.