Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:16:07.118850Z
Paper Citation Record · LEDGER
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation
As of 16 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2507.10591.
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
47 of 47 outbound references displayed
- verified exact0
- verified fuzzy46
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation afb6be65-5cef-4974-b28b-78052870fe07 · outbound
This paper cites A Comprehensive Survey on Feature Selection in the Various Fields of Machine Learning,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation A Comprehensive Survey on Feature Selection in the Various Fields of Machine Learning,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:15.250891Z
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 bc2c2e3f-157c-475e-8816-e09af5cba3b3 · outbound
This paper cites Importance of Features Selection, Attributes Selection, Challenges and Future Directions for Medical Imaging Data: A Review,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Importance of Features Selection, Attributes Selection, Challenges and Future Directions for Medical Imaging Data: A Review,
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:15.104881Z
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 37342dcc-f7f6-42e9-9622-b9542951aeea · outbound
This paper cites PermDroid: A Framework Developed Using Proposed Feature Selection Approach and Machine Learning Techniques for Android Malware Detection,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation PermDroid: A Framework Developed Using Proposed Feature Selection Approach and Machine Learning Techniques for Android Malware Detection,
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:14.976607Z
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 e6f8e4c0-8731-4197-9949-f4b976e83640 · outbound
This paper cites SemiDroid: A Behavioral Malware Detector Based on Unsupervised Machine Learning Techniques Using Feature Selection Approaches,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation SemiDroid: A Behavioral Malware Detector Based on Unsupervised Machine Learning Techniques Using Feature Selection Approaches,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:14.821668Z
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 93b44c02-ce6c-44a8-a3c8-26e3082f994d · outbound
This paper cites A New Feature Selection Method Based on a Self-Variant Genetic Algorithm Applied to Android Malware Detection,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation A New Feature Selection Method Based on a Self-Variant Genetic Algorithm Applied to Android Malware Detection,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:14.682231Z
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 36770c18-0f18-4371-9b9a-f10f88b5ac94 · outbound
This paper cites Significant API Calls in Android Malware Detection (Using Feature Selection Techniques and Correlation Based Feature Elimination),.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Significant API Calls in Android Malware Detection (Using Feature Selection Techniques and Correlation Based Feature Elimination),
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:14.541694Z
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 2565f3a8-c8d6-4b34-b18f-c09eb76791a4 · outbound
This paper cites Automated Malware Detection in Mobile App Stores Based on Robust Feature Generation,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Automated Malware Detection in Mobile App Stores Based on Robust Feature Generation,
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:14.355072Z
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 03c1eb77-ea26-4be6-b536-95583f936558 · outbound
This paper cites JOWMDroid: Android Malware Detection Based on Feature Weighting with Joint Optimization of Weight-Papping and Classifier Parameters,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation JOWMDroid: Android Malware Detection Based on Feature Weighting with Joint Optimization of Weight-Papping and Classifier Parameters,
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:14.179905Z
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 bf228f48-45a5-49d5-adec-f21adfcdf97c · outbound
This paper cites A Multi-Tiered Feature Selection Model for Android Malware Detection Based on Feature Discrimination and Information Gain,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation A Multi-Tiered Feature Selection Model for Android Malware Detection Based on Feature Discrimination and Information Gain,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:14.018237Z
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 34f65deb-6fc1-4779-bdf7-97ab0a7c1841 · outbound
This paper cites SigPID: Significant Per- mission Identification for Android Malware Detection,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation SigPID: Significant Per- mission Identification for Android Malware Detection,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:13.853846Z
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 462af7f0-9d70-4024-b374-f5292b83ba80 · outbound
This paper cites DroidRL: Feature Selection for Android Malware Detection with Reinforcement Learning,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation DroidRL: Feature Selection for Android Malware Detection with Reinforcement Learning,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:13.705684Z
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 d1329f06-7977-407a-a6e9-8efc96597b22 · outbound
This paper cites A Hybrid Feature Selection Approach-Based Android Malware Detection Framework Us- ing Machine Learning Techniques,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation A Hybrid Feature Selection Approach-Based Android Malware Detection Framework Us- ing Machine Learning Techniques,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:13.510608Z
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 a3e46828-9237-4ca5-8653-8a51e3529de8 · outbound
This paper cites BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation BFEDroid: A Feature Selection Technique to Detect Malware in Android Apps Using Machine Learning,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:13.316470Z
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 8090a82e-cb16-466d-a1d3-001e3e207378 · outbound
This paper cites A Novel Android Malware Detection System: Adaption of Filter-based Feature Selection Methods,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation A Novel Android Malware Detection System: Adaption of Filter-based Feature Selection Methods,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:13.131760Z
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 145ebefe-da08-46a9-a6c0-f473c7286b61 · outbound
This paper cites Captur- ing the Behavior of Android Malware with MH-100K: A Novel and Multidimensional Dataset,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Captur- ing the Behavior of Android Malware with MH-100K: A Novel and Multidimensional Dataset,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:12.981891Z
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 e7bcf895-d368-498b-9332-6a7ec2cda44a · outbound
This paper cites Detecc ¸˜ao de Malwares Android: Datasets e Reprodutibilidade,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Detecc ¸˜ao de Malwares Android: Datasets e Reprodutibilidade,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:12.806440Z
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 e4c3d562-9a8c-4c3c-b37b-dd5f892218b0 · outbound
This paper cites Debiasing Android Malware Datasets: How Can I Trust Your Results If Your Dataset Is Biased?.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Debiasing Android Malware Datasets: How Can I Trust Your Results If Your Dataset Is Biased?
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:12.663792Z
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 6f59d8ad-4fd8-470a-b2b9-cb84188a56f7 · outbound
This paper cites Effective and Efficient Android Malware Detection and Category Classification Using the Enhanced KronoDroid Dataset,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Effective and Efficient Android Malware Detection and Category Classification Using the Enhanced KronoDroid Dataset,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:12.524576Z
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 aade6402-70b5-424c-8110-b42d1f070d1b · outbound
This paper cites A Modified ResNeXt for Android Malware Identification and Classification,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation A Modified ResNeXt for Android Malware Identification and Classification,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:12.312494Z
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 bf1ceea1-0d9b-417e-9fad-f02f00799fd4 · outbound
This paper cites AndroOBFS: Time- tagged Obfuscated Android Malware Dataset with Family Information,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation AndroOBFS: Time- tagged Obfuscated Android Malware Dataset with Family Information,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:12.110309Z
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 39977380-963c-44ba-a5a9-4bd66ca11add · outbound
This paper cites Mal- Radar: Demystifying Android Malware in the New Era,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Mal- Radar: Demystifying Android Malware in the New Era,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:11.962821Z
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 4dedabc8-5481-4a37-a532-ff14f9748aa7 · outbound
This paper cites A Novel Android Malware Detection System: Adaption of Filter-Based Feature Selection Methods,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation A Novel Android Malware Detection System: Adaption of Filter-Based Feature Selection Methods,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:11.758607Z
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 37016af9-85ec-4b5e-adb5-c65880376c98 · outbound
This paper cites Android Malware Classification Using Optimum Feature Selection and Ensemble Machine Learning,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Android Malware Classification Using Optimum Feature Selection and Ensemble Machine Learning,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:11.611127Z
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 b2ab54d9-f2f7-4c7e-a360-d41ffbc69ee0 · outbound
This paper cites Deepdroid: Feature Selection Approach to Detect Android Malware Using Deep Learning,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Deepdroid: Feature Selection Approach to Detect Android Malware Using Deep Learning,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:11.419931Z
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 b6dfb835-5623-44e7-ade8-dd2e0eb4cd01 · outbound
This paper cites Fest: A Feature Extraction and Selection Tool for Android Malware Detection,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Fest: A Feature Extraction and Selection Tool for Android Malware Detection,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:11.258211Z
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 bbdee497-b357-4f09-a065-adab64fc2004 · outbound
This paper cites A Lightweight Android Malware Classifier Using Novel Feature Selection Methods,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation A Lightweight Android Malware Classifier Using Novel Feature Selection Methods,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:11.032625Z
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 8af1d482-68e7-48ae-a330-cd610885106b · outbound
This paper cites Android Malware Detection Using Genetic Algorithm Based Optimized Feature Selection and Machine Learning,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Android Malware Detection Using Genetic Algorithm Based Optimized Feature Selection and Machine Learning,
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:10.847307Z
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 c5ba4e9b-5cf6-4dd7-b016-f5849913a6fe · outbound
This paper cites Malware Detection Using Deep Learning and Correlation-Based Feature Selection,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Malware Detection Using Deep Learning and Correlation-Based Feature Selection,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:10.652271Z
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 07c199da-e9cd-48a0-961c-1f4c02a93f3e · outbound
This paper cites Opportunities and Challenges of Feature Selection Methods for High Dimensional Data: A Review,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Opportunities and Challenges of Feature Selection Methods for High Dimensional Data: A Review,
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:10.449354Z
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 99249285-e039-4135-8cc2-d15deccfbd3b · outbound
This paper cites A Survey on Intrusion Detection System: Feature Selection, Model, Performance Measures, Application Perspec- tive, Challenges, and Future Research Directions,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation A Survey on Intrusion Detection System: Feature Selection, Model, Performance Measures, Application Perspec- tive, Challenges, and Future Research Directions,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:10.190764Z
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 9afa99e2-fd82-4278-82a3-f70e961aba29 · outbound
This paper cites Uma An ´alise de M ´etodos de Selec ¸˜ao de Caracter ´ısticas Aplicados `a Detecc ¸˜ao de Malwares Android,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Uma An ´alise de M ´etodos de Selec ¸˜ao de Caracter ´ısticas Aplicados `a Detecc ¸˜ao de Malwares Android,
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:09.982380Z
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 1b4fe898-fcad-4786-9b31-0576625033ba · outbound
This paper cites FS3E: Uma Ferramenta para Execuc ¸ ˜ao e Avaliac ¸˜ao de M ´etodos de Selec ¸ ˜ao de Caracter ´ısticas para Detecc ¸ ˜ao de Malwares Android,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation FS3E: Uma Ferramenta para Execuc ¸ ˜ao e Avaliac ¸˜ao de M ´etodos de Selec ¸ ˜ao de Caracter ´ısticas para Detecc ¸ ˜ao de Malwares Android,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:09.813363Z
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 5868bb1a-df45-4609-8e66-37fbacbe4adc · outbound
This paper cites Avaliac ¸˜ao de M ´etodos de Selec ¸˜ao de Caracter ´ısticas de Amostras Android com a Ferramenta FS3E (v2),.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Avaliac ¸˜ao de M ´etodos de Selec ¸˜ao de Caracter ´ısticas de Amostras Android com a Ferramenta FS3E (v2),
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:09.627789Z
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 86ad76ff-20cd-4d9b-a2e1-6d863899bc6c · outbound
This paper cites MH-FSF: um Framework para Reproduc ¸˜ao, Experimentac ¸˜ao e Avaliac ¸˜ao de M ´etodos de Selec ¸˜ao de Caracter ´ısticas,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation MH-FSF: um Framework para Reproduc ¸˜ao, Experimentac ¸˜ao e Avaliac ¸˜ao de M ´etodos de Selec ¸˜ao de Caracter ´ısticas,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:09.462430Z
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 e404d640-a98b-42d1-b4a3-4b392199a6a4 · outbound
This paper cites A Comprehen- sive Survey: Artificial Bee Colony (ABC) Algorithm and Applications,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation A Comprehen- sive Survey: Artificial Bee Colony (ABC) Algorithm and Applications,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:09.290882Z
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 4e5b5fa4-7f96-44dd-963c-cf4ab5490ac9 · outbound
This paper cites Analysis of Variance (ANOV A),.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Analysis of Variance (ANOV A),
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:09.107132Z
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 a07cccaa-5c1e-4c96-8b6f-9f4801fdfcb2 · outbound
This paper cites Chi- Square Test,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Chi- Square Test,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:08.924129Z
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 3fd9a307-8ffc-4838-b91a-484f3a81ee83 · outbound
This paper cites Feature Selection Based on Information Gain,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Feature Selection Based on Information Gain,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:08.758814Z
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 02eca9c6-a311-4952-908e-edb85261c13f · outbound
This paper cites LASSO Regression,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation LASSO Regression,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:08.611037Z
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 2ea59982-1897-4e4f-9010-e2695b81a90a · outbound
This paper cites Mean-Absolute Deviation Model,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Mean-Absolute Deviation Model,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:08.422911Z
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 b6ea3d40-5f6a-40ca-9386-7170e8bae116 · outbound
This paper cites Principal Component Analysis (PCA),.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Principal Component Analysis (PCA),
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:08.249302Z
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 70a2a100-04c1-4442-a4a3-085a6d4b73a8 · outbound
This paper cites Pearson Correlation Coefficient,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Pearson Correlation Coefficient,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:08.000233Z
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 90fa3ad0-49f5-4cff-ae0b-7429cf815e5b · outbound
This paper cites Theoretical and Empirical Analysis of ReliefF and RReliefF,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Theoretical and Empirical Analysis of ReliefF and RReliefF,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:07.774072Z
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 8c13cd35-7867-48ef-b0f0-1e1691135daa · outbound
This paper cites Using Recursive Feature Elimination in Random Forest to Account for Correlated Variables in High Dimensional Data,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Using Recursive Feature Elimination in Random Forest to Account for Correlated Variables in High Dimensional Data,
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:07.605180Z
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 75ed2d30-0619-480d-8571-477a1a79684f · outbound
This paper cites Scikit-learn: Machine Learning in Python,.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation Scikit-learn: Machine Learning in Python,
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:07.453777Z
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 b818555b-4ec6-4f70-b4df-8359792493ed · outbound
This paper cites The MCC-F1 curve: a performance evaluation technique for binary classification.
MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation The MCC-F1 curve: a performance evaluation technique for binary classification
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:16:07.049476Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 413f9ebc-a3ce-4b4e-a49f-ffa882832609 · outbound
This paper cites James, D.
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:16:07.287858Z
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.