MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation | Paper Citation Record

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

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+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

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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 2026-08-17T06:30:58.91139+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

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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

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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

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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 2026-08-17T06:30:58.91139+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

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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

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MH-FSF: A Unified Framework for Overcoming Benchmarking and Reproducibility Limitations in Feature Selection Evaluation James, D

Reference 47

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