tag:joss.theoj.org,2005:/papers/tagged/Feature%20SelectionJournal of Open Source Software2023-12-12T17:00:34ZJournal of Open Source Softwarehttps://joss.theoj.orgtag:joss.theoj.org,2005:Paper/45902023-12-12T17:00:34Z2023-12-13T00:00:42Zpudu: A Python library for agnostic feature selection and explainability of Machine Learning spectroscopic problemsaccepted0.3.02023-07-10 08:59:28 UTC922023-12-12 17:00:34 UTC820235873EnricGrau-LuqueCatalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 08930 Sant Adrià de Besòs, Spain.0000-0002-8357-5824IgnacioBecerril-RomeroCatalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 08930 Sant Adrià de Besòs, Spain.0000-0002-7087-6097AlejandroPerez-RodriguezCatalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 08930 Sant Adrià de Besòs, Spain., Departament d'Enginyeria Electrònica i Biomèdica, IN2UB, Universitat de Barcelona, C/ Martí i Franqués 1, 08028 Barcelona, Spain.0000-0002-3634-1355MaximGucCatalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 08930 Sant Adrià de Besòs, Spain.0000-0002-2072-9566VictorIzquierdo-RocaCatalonia Institute for Energy Research (IREC), Jardins de les Dones de Negre 1, 08930 Sant Adrià de Besòs, Spain.0000-0002-5502-313310.21105/joss.05873https://doi.org/10.5281/zenodo.10161346Pythonhttps://joss.theoj.org/papers/10.21105/joss.05873.pdfSpectroscopy, Machine Learning, Explainability and intepretability, Classification and regressiontag:joss.theoj.org,2005:Paper/41462023-04-23T06:22:55Z2023-04-27T15:33:07Zpysr3: A Python Package for Sparse Relaxed Regularized Regressionacceptedv0.3.32023-01-04 18:36:29 UTC842023-04-23 06:22:55 UTC820235155AlekseiSholokhovDepartment of Applied Mathematics, University of Washington0000-0001-8173-6236PengZhengDepartment of Health Metrics Sciences, University of Washington0000-0003-3313-215XAleksandrAravkinDepartment of Applied Mathematics, University of Washington, Department of Health Metrics Sciences, University of Washington0000-0002-1875-180110.21105/joss.05155https://doi.org/10.5281/zenodo.7839335Pythonhttps://joss.theoj.org/papers/10.21105/joss.05155.pdffeature selection, linear models, mixed-effect models, regularizationtag:joss.theoj.org,2005:Paper/39332023-01-27T13:07:55Z2023-01-28T00:01:25ZUBayFS: An R Package for User Guided Feature Selectionacceptedv1.0.02022-10-01 18:24:24 UTC812023-01-27 13:07:55 UTC820234848AnnaJenulNorwegian University of Life Sciences, Ås, Norway0000-0002-6919-3483StefanSchrunnerNorwegian University of Life Sciences, Ås, Norway0000-0003-1327-485510.21105/joss.04848https://doi.org/10.5281/zenodo.7554373Rhttps://joss.theoj.org/papers/10.21105/joss.04848.pdffeature selectiontag:joss.theoj.org,2005:Paper/37122022-11-23T18:54:13Z2022-11-25T13:54:46Zfseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithmsacceptedv3.0.22022-07-11 12:22:23 UTC792022-11-23 18:54:13 UTC720224611Jeroen G. S.OverschieBernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, P.O. Box 407, 9700 AK Groningen, The Netherlands0000-0003-3304-3800AhmadAlsahafDepartment of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands0000-0002-0770-1390GeorgeAzzopardiBernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, P.O. Box 407, 9700 AK Groningen, The Netherlands0000-0001-6552-259610.21105/joss.04611https://doi.org/10.5281/zenodo.7343417https://joss.theoj.org/papers/10.21105/joss.04611.pdffeature ranking, feature selection, benchmarking, machine learning, open-source, software, pythontag:joss.theoj.org,2005:Paper/29332021-09-22T14:59:09Z2021-09-27T12:54:48ZFeature-engine: A Python package for feature engineering for machine learningacceptedv1.1.12021-08-09 09:18:42 UTC652021-09-22 14:59:09 UTC620213642SoledadGalliTrain in Data10.21105/joss.03642https://doi.org/10.5281/zenodo.5515531Pythonhttps://joss.theoj.org/papers/10.21105/joss.03642.pdfpython, feature engineering, feature selection, machine learning, data sciencetag:joss.theoj.org,2005:Paper/26062021-07-28T17:56:07Z2021-07-29T00:03:16ZRENT: A Python Package for Repeated Elastic Net Feature Selectionaccepted0.0.12021-04-29 19:17:35 UTC632021-07-28 17:56:07 UTC620213323AnnaJenulDepartment of Data Science, Norwegian University of Life Sciences0000-0002-6919-3483StefanSchrunnerDepartment of Data Science, Norwegian University of Life Sciences0000-0003-1327-4855BaoNgocHuynhDepartment of Physics, Norwegian University of Life Sciences0000-0001-5210-132XOliverTomicDepartment of Data Science, Norwegian University of Life Sciences0000-0003-1595-996210.21105/joss.03323https://doi.org/10.5281/zenodo.5140349Pythonhttps://joss.theoj.org/papers/10.21105/joss.03323.pdffeature selectiontag:joss.theoj.org,2005:Paper/22262021-03-31T15:41:29Z2021-04-01T07:34:56Zstabm: Stability Measures for Feature Selectionaccepted1.1.42020-12-09 07:40:30 UTC592021-03-31 15:41:29 UTC620213010AndreaBommertFaculty of Statistics, TU Dortmund University, 44221 Dortmund, Germany0000-0002-1005-9351MichelLangFaculty of Statistics, TU Dortmund University, 44221 Dortmund, Germany0000-0001-9754-039310.21105/joss.03010https://doi.org/10.5281/zenodo.4648087Rhttps://joss.theoj.org/papers/10.21105/joss.03010.pdffeature selection stability, stability measures, similarity measurestag:joss.theoj.org,2005:Paper/14002020-05-10T21:20:37Z2021-02-15T11:31:15ZLFSpy: A Python Implementation of Local Feature Selection for Data Classification with scikit-learn Compatibilityaccepted1.0.02019-12-12 04:33:33 UTC492020-05-10 21:20:37 UTC520201958KiretDhindsaResearch and High Performance Computing, McMaster University, Vector Institute, Department of Surgery, McMaster University0000-0003-4849-732XOliverCookResearch and High Performance Computing, McMaster University0000-0002-5511-094XThomasMudwayResearch and High Performance Computing, McMaster University0000-0001-6213-2111AreebKhawajaResearch and High Performance Computing, McMaster University0000-0003-4528-9146RonHarwoodResearch and High Performance Computing, McMaster University0000-0002-7922-0641RanilSonnadaraResearch and High Performance Computing, McMaster University, Vector Institute, Department of Surgery, McMaster University0000-0001-8318-571410.21105/joss.01958https://doi.org/10.5281/zenodo.3813708Pythonhttps://joss.theoj.org/papers/10.21105/joss.01958.pdfMachine Learning, Feature Selection, Classification, Data Sciencetag:joss.theoj.org,2005:Paper/12932020-01-23T00:27:17Z2021-02-15T11:31:30ZMOAFS: A Massive Online Analysis library for feature selection in data streamsacceptedv1.0.02019-10-15 21:35:53 UTC452020-01-23 00:27:17 UTC520201970MatheusBernardellide MoraesFaculty of Technology, University of Campinas0000-0002-9485-0334AndréLeon SampaioGradvohlFaculty of Technology, University of Campinas0000-0002-6520-974010.21105/joss.01970https://doi.org/10.6084/m9.figshare.11663307.v2Javahttps://joss.theoj.org/papers/10.21105/joss.01970.pdffeature selection, data streams, concept drift, moatag:joss.theoj.org,2005:Paper/4392018-04-22T13:05:17Z2021-02-15T11:33:32ZMLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stackacceptedv0.11.02018-03-15 16:08:53 UTC242018-04-22 13:05:17 UTC32018638SebastianRaschkaMichigan State University0000-0001-6989-449310.21105/joss.00638https://doi.org/10.5281/zenodo.1226560Pythonhttps://joss.theoj.org/papers/10.21105/joss.00638.pdfmachine learning, data science, association rule mining, ensemble learning, feature selection