Paper Citation Record · LEDGER

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study

As of 15 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2501.00532.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.00532 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:54:17.215438Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact2
verified fuzzy27
unresolved3
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 740e273d-3c0b-4c63-a576-187e853e2069 · outbound

This paper cites Current approaches for executing big data science projects—a systematic literature review,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Current approaches for executing big data science projects—a systematic literature review,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.546019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 98c58ea2-21cf-4c27-88ed-2e9b097b0286 · outbound

This paper cites Towards automated variability-aware machine-learning-based modeling analysis,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Towards automated variability-aware machine-learning-based modeling analysis,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.536486Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3ceab17e-7977-4ba9-95d9-296395be382b · outbound

This paper cites Adaptive method for machine learning model selection in data science projects,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Adaptive method for machine learning model selection in data science projects,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.527251Z

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This paper cites Variability in software systems—a systematic literature review,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Variability in software systems—a systematic literature review,

Reference 4

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This paper cites A taxonomy of variability realization techniques,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study A taxonomy of variability realization techniques,

Reference 5

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Observation ae9ffdf2-116c-443c-bacc-59c28bbea443 · outbound

This paper cites Ruva: A runtime software variability algorithm,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Ruva: A runtime software variability algorithm,

Reference 6

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Observation 663cfea5-1c6a-4a42-b3c1-eb4da6b143c9 · outbound

This paper cites B ¨uhne and K.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study B ¨uhne and K

Reference 7

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3cd32a00-38ec-48ff-91df-dd251892631b · outbound

This paper cites A variability model for query optimizers,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study A variability model for query optimizers,

Reference 8

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This paper cites Spl driven approach for vari- ability in database design,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Spl driven approach for vari- ability in database design,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.479615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 98275b81-8043-44f8-a7cc-4d0d20199c43 · outbound

This paper cites Kang, Kyo C.and Lee, Variability Modeling.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Kang, Kyo C.and Lee, Variability Modeling

Reference 10

Resolution: verified exact; doi, observed 2026-08-10T22:54:17.245398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9a11bcad-1c9c-4f80-98eb-813236d256a2 · outbound

This paper cites Crisp-dm 1.0: Step-by-step data mining guide,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Crisp-dm 1.0: Step-by-step data mining guide,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.469851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f8af1329-8616-400c-a0fe-c54073303a65 · outbound

This paper cites An overview of the machine learning applied in smart cities,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study An overview of the machine learning applied in smart cities,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.459700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 235f4d46-2a1d-43bf-87fe-6cd889fa955d · outbound

This paper cites Automated Machine Learning: From Principles to Practices.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Automated Machine Learning: From Principles to Practices

Reference 13

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Source-reported events for the cited work

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This paper cites Photonai—a python api for rapid machine learning model development,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Photonai—a python api for rapid machine learning model development,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.450286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f430398d-3d20-4bd8-835d-76f2cf11ca34 · outbound

This paper cites Scikit-learn: Machine learning in python,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Scikit-learn: Machine learning in python,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.439928Z

Source-reported events for the cited work

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Observation f4d56329-e455-47bb-917c-cd4b5c2a5364 · outbound

This paper cites Machine learning algorithm cheat sheet for azure machine learning designer,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Machine learning algorithm cheat sheet for azure machine learning designer,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.430522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a8d1c4bd-d160-45c4-8693-6c5fba51c381 · outbound

This paper cites What is a feature? a qualitative study of features in industrial software product lines,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study What is a feature? a qualitative study of features in industrial software product lines,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.421176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c7a263bb-1b8c-45f4-8381-24af5d258ac3 · outbound

This paper cites Mlops- definitions, tools and challenges,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Mlops- definitions, tools and challenges,

Reference 18

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Observation e26db284-3ddd-4b32-bd5a-bd22e5af6b37 · outbound

This paper cites Scalable architecture for automating machine learning model monitoring,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Scalable architecture for automating machine learning model monitoring,

Reference 19

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 18a9b496-a961-43b0-83fc-26aeed825940 · outbound

This paper cites Context-aware data ana- lytics variability in iot neural network-based systems,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Context-aware data ana- lytics variability in iot neural network-based systems,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.392948Z

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Observation ebea9425-2a8e-4d90-979c-088ac12da8f5 · outbound

This paper cites Project, EDISON Data Science Framework (EDSF).

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Project, EDISON Data Science Framework (EDSF)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.383101Z

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Observation 55598dc5-f0dc-4d0a-b2f9-cbc03329cee9 · outbound

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Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Machine learning algorithms and their relationship with modern technologies,

Reference 22

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This paper cites Explainability of a machine learning granting scoring model in peer- to-peer lending,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Explainability of a machine learning granting scoring model in peer- to-peer lending,

Reference 23

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Observation b83684c4-8c25-4839-839e-ed637c8da505 · outbound

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Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.353233Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 34ba4aa2-c8dd-4087-a0d3-1f6546328147 · outbound

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Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.342816Z

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Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Deequ-data quality validation for machine learning pipelines,

Reference 26

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Source-reported events for the cited work

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This paper cites Modelops: Cloud-based lifecycle management for reliable and trusted ai,.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Modelops: Cloud-based lifecycle management for reliable and trusted ai,

Reference 27

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Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Learning under concept drift: A review,

Reference 28

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Source-reported events for the cited work

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Observation 1e7cfd93-cbcb-4404-8dab-dde04c199c61 · outbound

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Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Automl to date and beyond: Challenges and opportunities,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.302106Z

Source-reported events for the cited work

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Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Software engineering for machine learning: A case study,

Reference 30

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Observation 8d36ffb7-de29-4f49-bf78-b8d04e997f44 · outbound

This paper cites Monitoring and explainability of models in production.

Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Monitoring and explainability of models in production

Reference 31

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Source-reported events for the cited work

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Observation 2fbc2de2-f31c-4931-b7c3-e953921c91eb · outbound

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Variability-Aware Machine Learning Model Selection: Feature Modeling, Instantiation, and Experimental Case Study Ensemble classifiers: Api reference,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:54:17.285388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

No inbound Pith citation observations are available.