Stream-learn — open-source Python library for difficult data stream batch analysis
Stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a stream generator, which allows producing a synthetic data stream that may incorporate each of the three main concept drift types (i.e., sudden, gr...
Gespeichert in:
| Veröffentlicht in: | Neurocomputing (Amsterdam) Jg. 478; S. 11 - 21 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
14.03.2022
|
| Schlagworte: | |
| ISSN: | 0925-2312, 1872-8286 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a stream generator, which allows producing a synthetic data stream that may incorporate each of the three main concept drift types (i.e., sudden, gradual and incremental drift) in their recurring or non-recurring version, as well as static and dynamic class imbalance. The package allows conducting experiments following established evaluation methodologies (i.e., Test-Then-Train and Prequential). Besides, estimators adapted for data stream classification have been implemented, including both simple classifiers and state-of-the-art chunk-based and online classifier ensembles. The package utilises its own implementations of prediction metrics for imbalanced binary classification tasks to improve computational efficiency. |
|---|---|
| AbstractList | Stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a stream generator, which allows producing a synthetic data stream that may incorporate each of the three main concept drift types (i.e., sudden, gradual and incremental drift) in their recurring or non-recurring version, as well as static and dynamic class imbalance. The package allows conducting experiments following established evaluation methodologies (i.e., Test-Then-Train and Prequential). Besides, estimators adapted for data stream classification have been implemented, including both simple classifiers and state-of-the-art chunk-based and online classifier ensembles. The package utilises its own implementations of prediction metrics for imbalanced binary classification tasks to improve computational efficiency. |
| Author | Zyblewski, P. Ksieniewicz, P. |
| Author_xml | – sequence: 1 givenname: P. surname: Ksieniewicz fullname: Ksieniewicz, P. – sequence: 2 givenname: P. surname: Zyblewski fullname: Zyblewski, P. |
| BookMark | eNqFkM1KAzEUhYNUsK2-gYu8wIxJ5t-FIMU_KChY1-FOcoemTJOSpEJ3PoRP6JM4bV250NWFD77DuWdCRtZZJOSSs5QzXl6tUotb5dapYIKneyrYCRnzuhJJLepyRMasEUUiMi7OyCSEFWO84qIZk8Vr9AjrpEfwln59fFK3QZsEt_UK6csuLp2lvWk9-B3tnKfadJ1R2z5SDRFoOOi0haiWFCz0u2DCOTntoA948XOn5O3-bjF7TObPD0-z23miMlbGBJhC1mRZxbhuhOhqXeSFrjRnWAhdQluphmk98DxvVQ6dyFoOTaaA14LnOpuS_JirvAvBYyc33qyHppIzuV9GruRxGblf5kAFG7TrX5oyEaJxNnow_X_yzVHG4bF3g14GZdAq1MajilI783fAN3Mrhgc |
| CitedBy_id | crossref_primary_10_1109_TII_2022_3217541 crossref_primary_10_1109_TNSM_2024_3435516 crossref_primary_10_1109_ACCESS_2023_3295694 crossref_primary_10_1007_s10994_023_06353_6 crossref_primary_10_3390_electronics11233962 crossref_primary_10_1016_j_ins_2023_02_046 crossref_primary_10_1007_s11227_025_07482_6 crossref_primary_10_1007_s11042_024_19737_0 crossref_primary_10_1007_s10994_024_06612_0 crossref_primary_10_1016_j_neucom_2023_126554 crossref_primary_10_3390_machines11020151 crossref_primary_10_1016_j_ins_2024_120555 crossref_primary_10_1016_j_neucom_2024_128194 |
| Cites_doi | 10.1016/B978-0-12-804291-5.00024-6 10.1016/S0031-3203(02)00257-1 10.1109/IJCNN52387.2021.9533795 10.1109/ICSMC.2005.1571498 10.1016/j.inffus.2020.09.004 10.1007/s13748-016-0094-0 10.1007/978-3-642-01091-0_2 10.1016/j.inffus.2017.02.004 10.1145/956750.956778 10.1109/IJCNN48605.2020.9207498 10.1016/j.neucom.2018.05.130 10.1201/EBK1439826119-c1 10.1007/s10994-012-5320-9 10.1016/j.ins.2018.06.020 10.1109/TKDE.2014.2345380 |
| ContentType | Journal Article |
| Copyright | 2022 Elsevier B.V. |
| Copyright_xml | – notice: 2022 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.neucom.2021.10.120 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1872-8286 |
| EndPage | 21 |
| ExternalDocumentID | 10_1016_j_neucom_2021_10_120 S0925231222000108 |
| GroupedDBID | --- --K --M .DC .~1 0R~ 123 1B1 1~. 1~5 4.4 457 4G. 53G 5VS 7-5 71M 8P~ 9JM 9JN AABNK AACTN AADPK AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AAXLA AAXUO AAYFN ABBOA ABCQJ ABFNM ABJNI ABMAC ABYKQ ACDAQ ACGFS ACRLP ACZNC ADBBV ADEZE AEBSH AEKER AENEX AFKWA AFTJW AFXIZ AGHFR AGUBO AGWIK AGYEJ AHHHB AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD AXJTR BKOJK BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ IHE J1W KOM LG9 M41 MO0 MOBAO N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. Q38 ROL RPZ SDF SDG SDP SES SPC SPCBC SSN SSV SSZ T5K ZMT ~G- 29N 9DU AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB HLZ HVGLF HZ~ R2- SBC SEW WUQ XPP ~HD |
| ID | FETCH-LOGICAL-c306t-a0ce0933701d922f8d545d7d10e52d6ab7c90ddf8d44bc4af23b1a93ca18214d3 |
| ISICitedReferencesCount | 18 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000761815800002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0925-2312 |
| IngestDate | Sat Nov 29 07:13:35 EST 2025 Tue Nov 18 22:23:56 EST 2025 Fri Feb 23 02:40:50 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Imbalanced data Data stream Dynamic class imbalance Concept drift |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c306t-a0ce0933701d922f8d545d7d10e52d6ab7c90ddf8d44bc4af23b1a93ca18214d3 |
| PageCount | 11 |
| ParticipantIDs | crossref_primary_10_1016_j_neucom_2021_10_120 crossref_citationtrail_10_1016_j_neucom_2021_10_120 elsevier_sciencedirect_doi_10_1016_j_neucom_2021_10_120 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-03-14 |
| PublicationDateYYYYMMDD | 2022-03-14 |
| PublicationDate_xml | – month: 03 year: 2022 text: 2022-03-14 day: 14 |
| PublicationDecade | 2020 |
| PublicationTitle | Neurocomputing (Amsterdam) |
| PublicationYear | 2022 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel, Blondel, Prettenhofer, Weiss, Dubourg, Vanderplas, Passos, Cournapeau, Brucher, Perrot, Duchesnay (b0020) 2011; 12 Appendix b - the weka workbench, in: I.H. Witten, E. Frank, M.A. Hall, C.J. Pal (Eds.), Data Mining (Fourth Edition), fourth edition Edition, Morgan Kaufmann, 2017, pp. 553 – 571. Powers (b0090) 2011; 2 Kelleher, Namee, D’Arcy (b0110) 2015 Krawczyk (b0015) 2016; 5 J. Gama, P.P. Rodrigues, An overview on mining data streams, in: Foundations of Computational, IntelligenceVolume 6, Springer, 2009, pp. 29–45. Woźniak, Kasprzak, Cal (b0080) 2013 J. Gama, Knowledge Discovery from Data Streams, 1st Edition, Chapman Hall/CRC, 2010. Street, Kim (b0055) 2001 Brodersen, Ong, Stephan, Buhmann (b0105) 2010 Krawczyk, Minku, Gama, Stefanowski, Wozniak (b0010) 2017; 37 Montiel, Read, Bifet, Abdessalem (b0035) 2018; 19 M. Kubat, S. Matwin, Addressing the curse of imbalanced training sets: One-sided selection, in: ICML, 1997. P. Ksieniewicz, M. Woźniak, B. Cyganek, A. Kasprzak, K. Walkowiak, Data stream classification using active learned neural networks, Neurocomputing 353 (2019) 74–82, recent Advancements in Hybrid Artificial Intelligence Systems. Zyblewski, Ksieniewicz, Woźniak (b0150) 2019 Baeza-Yates, Ribeiro-Neto (b0095) 1999 N.C. Oza, Online bagging and boosting, in: 2005 IEEE International Conference on Systems, Man and Cybernetics, Vol. 3, 2005, pp. 2340–2345 Vol. 3. Y. Sasaki, The truth of the f-measure, Teach Tutor Mater. H. Wang, W. Fan, P.S. Yu, J. Han, Mining concept-drifting data streams using ensemble classifiers, in: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’03, ACM, New York, NY, USA, 2003, pp. 226–235. Wang, Minku, Yao (b0065) 2015; 27 Guyon (b0050) 2003 Zyblewski, Sabourin, Woźniak (b0160) 2021; 66 P. Ksieniewicz, P. Zyblewski, M. Choraś, R. Kozik, A. Giełczyk, M. Woźniak, Fake news detection from data streams, in: 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 2020, pp. 1–8. R.M.O. Cruz, L.G. Hafemann, R. Sabourin, G.D.C. Cavalcanti, DESlib: A Dynamic ensemble selection library in Python, arXiv preprint arXiv:1802.04967. Visual-based analysis of classification measures and their properties for class imbalanced problems, Information Sciences 462 (2018) 242 – 261. Zyblewski, Sabourin, Woźniak (b0155) 2020 Bifet, Holmes, Kirkby, Pfahringer (b0040) 2010; 11 Barandela, Sánchez, García, Rangel (b0115) 2003; 36 J. Komorniczak, P. Zyblewski, P. Ksieniewicz, Prior probability estimation in dynamically imbalanced data streams, in: The International Joint Conference on Neural Networks, 2021. Gama, Sebastião, Rodrigues (b0135) 2013; 90 Gulowaty, Ksieniewicz (b0140) 2019 Lemaıˇtre, Nogueira, Aridas (b0025) 2017; 18 Brzeziński, Stefanowski (b0075) 2011 Brodersen (10.1016/j.neucom.2021.10.120_b0105) 2010 Krawczyk (10.1016/j.neucom.2021.10.120_b0010) 2017; 37 10.1016/j.neucom.2021.10.120_b0030 Wang (10.1016/j.neucom.2021.10.120_b0065) 2015; 27 10.1016/j.neucom.2021.10.120_b0130 Kelleher (10.1016/j.neucom.2021.10.120_b0110) 2015 10.1016/j.neucom.2021.10.120_b0070 Gulowaty (10.1016/j.neucom.2021.10.120_b0140) 2019 Krawczyk (10.1016/j.neucom.2021.10.120_b0015) 2016; 5 Montiel (10.1016/j.neucom.2021.10.120_b0035) 2018; 19 Powers (10.1016/j.neucom.2021.10.120_b0090) 2011; 2 Baeza-Yates (10.1016/j.neucom.2021.10.120_b0095) 1999 Woźniak (10.1016/j.neucom.2021.10.120_b0080) 2013 Gama (10.1016/j.neucom.2021.10.120_b0135) 2013; 90 Bifet (10.1016/j.neucom.2021.10.120_b0040) 2010; 11 Street (10.1016/j.neucom.2021.10.120_b0055) 2001 Zyblewski (10.1016/j.neucom.2021.10.120_b0155) 2020 Zyblewski (10.1016/j.neucom.2021.10.120_b0160) 2021; 66 Barandela (10.1016/j.neucom.2021.10.120_b0115) 2003; 36 10.1016/j.neucom.2021.10.120_b0125 10.1016/j.neucom.2021.10.120_b0005 10.1016/j.neucom.2021.10.120_b0120 10.1016/j.neucom.2021.10.120_b0165 10.1016/j.neucom.2021.10.120_b0045 10.1016/j.neucom.2021.10.120_b0100 Brzeziński (10.1016/j.neucom.2021.10.120_b0075) 2011 10.1016/j.neucom.2021.10.120_b0145 10.1016/j.neucom.2021.10.120_b0085 Lemaıˇtre (10.1016/j.neucom.2021.10.120_b0025) 2017; 18 10.1016/j.neucom.2021.10.120_b0060 Pedregosa (10.1016/j.neucom.2021.10.120_b0020) 2011; 12 Guyon (10.1016/j.neucom.2021.10.120_b0050) 2003 Zyblewski (10.1016/j.neucom.2021.10.120_b0150) 2019 |
| References_xml | – start-page: 367 year: 2020 end-page: 379 ident: b0155 article-title: Data preprocessing and dynamic ensemble selection for imbalanced data stream classification publication-title: Machine Learning and Knowledge Discovery in Databases – volume: 18 start-page: 1 year: 2017 end-page: 5 ident: b0025 article-title: Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning publication-title: J. Mach. Learn. Res. – volume: 5 start-page: 221 year: 2016 end-page: 232 ident: b0015 article-title: Learning from imbalanced data: open challenges and future directions publication-title: Progress Artif. Intell. – volume: 19 start-page: 1 year: 2018 end-page: 5 ident: b0035 article-title: Scikit-multiflow: A multi-output streaming framework publication-title: J. Mach. Learn. Res. – reference: P. Ksieniewicz, P. Zyblewski, M. Choraś, R. Kozik, A. Giełczyk, M. Woźniak, Fake news detection from data streams, in: 2020 International Joint Conference on Neural Networks (IJCNN), IEEE, 2020, pp. 1–8. – volume: 36 start-page: 849 year: 2003 end-page: 851 ident: b0115 article-title: Strategies for learning in class imbalance problems publication-title: Pattern Recogn. – reference: J. Gama, Knowledge Discovery from Data Streams, 1st Edition, Chapman Hall/CRC, 2010. – year: 2015 ident: b0110 article-title: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies – year: 1999 ident: b0095 article-title: Modern Information Retrieval – volume: 11 start-page: 1601 year: 2010 end-page: 1604 ident: b0040 article-title: MOA: massive online analysis publication-title: J. Mach. Learn. Res. – start-page: 3121 year: 2010 end-page: 3124 ident: b0105 article-title: The balanced accuracy and its posterior distribution publication-title: Proceedings of the 2010 20th International Conference on Pattern Recognition, ICPR ’10 – volume: 90 start-page: 317 year: 2013 end-page: 346 ident: b0135 article-title: On evaluating stream learning algorithms publication-title: Machine Learning – start-page: 626 year: 2019 end-page: 635 ident: b0150 article-title: Classifier selection for highly imbalanced data streams with minority driven ensemble publication-title: Artificial Intelligence and Soft Computing – reference: N.C. Oza, Online bagging and boosting, in: 2005 IEEE International Conference on Systems, Man and Cybernetics, Vol. 3, 2005, pp. 2340–2345 Vol. 3. – volume: 12 start-page: 2825 year: 2011 end-page: 2830 ident: b0020 article-title: Scikit-learn: Machine learning in Python publication-title: J. Mach. Learn. Res. – volume: 2 start-page: 2229 year: 2011 end-page: 3981 ident: b0090 article-title: Ailab, Evaluation: From precision, recall and f-measure to roc, informedness, markedness correlation publication-title: J. Mach. Learn. Technol – reference: J. Komorniczak, P. Zyblewski, P. Ksieniewicz, Prior probability estimation in dynamically imbalanced data streams, in: The International Joint Conference on Neural Networks, 2021. – start-page: 579 year: 2013 end-page: 588 ident: b0080 article-title: Weighted aging classifier ensemble for the incremental drifted data streams publication-title: Flexible Query Answering Systems – start-page: 305 year: 2019 end-page: 312 ident: b0140 article-title: Smote algorithm variations in balancing data streams publication-title: Intelligent Data Engineering and Automated Learning – IDEAL 2019 – volume: 27 start-page: 1356 year: 2015 end-page: 1368 ident: b0065 article-title: Resampling-based ensemble methods for online class imbalance learning publication-title: IEEE Trans. Knowl. Data Eng. – reference: R.M.O. Cruz, L.G. Hafemann, R. Sabourin, G.D.C. Cavalcanti, DESlib: A Dynamic ensemble selection library in Python, arXiv preprint arXiv:1802.04967. – volume: 37 start-page: 132 year: 2017 end-page: 156 ident: b0010 article-title: Ensemble learning for data stream analysis: A survey publication-title: Inform. Fusion – year: 2003 ident: b0050 article-title: Design of experiments of the nips 2003 variable selection benchmark publication-title: NIPS 2003 workshop on feature extraction and feature selection – volume: 66 start-page: 138 year: 2021 end-page: 154 ident: b0160 article-title: Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams publication-title: Information Fusion – start-page: 377 year: 2001 end-page: 382 ident: b0055 article-title: A streaming ensemble algorithm (sea) for large-scale classification publication-title: Proceedings of the 7Th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining – reference: H. Wang, W. Fan, P.S. Yu, J. Han, Mining concept-drifting data streams using ensemble classifiers, in: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’03, ACM, New York, NY, USA, 2003, pp. 226–235. – start-page: 155 year: 2011 end-page: 163 ident: b0075 article-title: Accuracy updated ensemble for data streams with concept drift publication-title: Hybrid Artificial Intelligent Systems – reference: P. Ksieniewicz, M. Woźniak, B. Cyganek, A. Kasprzak, K. Walkowiak, Data stream classification using active learned neural networks, Neurocomputing 353 (2019) 74–82, recent Advancements in Hybrid Artificial Intelligence Systems. – reference: J. Gama, P.P. Rodrigues, An overview on mining data streams, in: Foundations of Computational, IntelligenceVolume 6, Springer, 2009, pp. 29–45. – reference: Visual-based analysis of classification measures and their properties for class imbalanced problems, Information Sciences 462 (2018) 242 – 261.] – reference: M. Kubat, S. Matwin, Addressing the curse of imbalanced training sets: One-sided selection, in: ICML, 1997. – reference: Y. Sasaki, The truth of the f-measure, Teach Tutor Mater. – reference: Appendix b - the weka workbench, in: I.H. Witten, E. Frank, M.A. Hall, C.J. Pal (Eds.), Data Mining (Fourth Edition), fourth edition Edition, Morgan Kaufmann, 2017, pp. 553 – 571. – year: 2003 ident: 10.1016/j.neucom.2021.10.120_b0050 article-title: Design of experiments of the nips 2003 variable selection benchmark – volume: 19 start-page: 1 issue: 72 year: 2018 ident: 10.1016/j.neucom.2021.10.120_b0035 article-title: Scikit-multiflow: A multi-output streaming framework publication-title: J. Mach. Learn. Res. – start-page: 305 year: 2019 ident: 10.1016/j.neucom.2021.10.120_b0140 article-title: Smote algorithm variations in balancing data streams – volume: 2 start-page: 2229 year: 2011 ident: 10.1016/j.neucom.2021.10.120_b0090 article-title: Ailab, Evaluation: From precision, recall and f-measure to roc, informedness, markedness correlation publication-title: J. Mach. Learn. Technol – ident: 10.1016/j.neucom.2021.10.120_b0045 doi: 10.1016/B978-0-12-804291-5.00024-6 – start-page: 579 year: 2013 ident: 10.1016/j.neucom.2021.10.120_b0080 article-title: Weighted aging classifier ensemble for the incremental drifted data streams – volume: 36 start-page: 849 year: 2003 ident: 10.1016/j.neucom.2021.10.120_b0115 article-title: Strategies for learning in class imbalance problems publication-title: Pattern Recogn. doi: 10.1016/S0031-3203(02)00257-1 – start-page: 626 year: 2019 ident: 10.1016/j.neucom.2021.10.120_b0150 article-title: Classifier selection for highly imbalanced data streams with minority driven ensemble – start-page: 3121 year: 2010 ident: 10.1016/j.neucom.2021.10.120_b0105 article-title: The balanced accuracy and its posterior distribution – ident: 10.1016/j.neucom.2021.10.120_b0125 doi: 10.1109/IJCNN52387.2021.9533795 – ident: 10.1016/j.neucom.2021.10.120_b0060 doi: 10.1109/ICSMC.2005.1571498 – ident: 10.1016/j.neucom.2021.10.120_b0100 – volume: 18 start-page: 1 issue: 17 year: 2017 ident: 10.1016/j.neucom.2021.10.120_b0025 article-title: Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning publication-title: J. Mach. Learn. Res. – volume: 66 start-page: 138 year: 2021 ident: 10.1016/j.neucom.2021.10.120_b0160 article-title: Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams publication-title: Information Fusion doi: 10.1016/j.inffus.2020.09.004 – start-page: 367 year: 2020 ident: 10.1016/j.neucom.2021.10.120_b0155 article-title: Data preprocessing and dynamic ensemble selection for imbalanced data stream classification – volume: 5 start-page: 221 issue: 4 year: 2016 ident: 10.1016/j.neucom.2021.10.120_b0015 article-title: Learning from imbalanced data: open challenges and future directions publication-title: Progress Artif. Intell. doi: 10.1007/s13748-016-0094-0 – ident: 10.1016/j.neucom.2021.10.120_b0005 doi: 10.1007/978-3-642-01091-0_2 – start-page: 155 year: 2011 ident: 10.1016/j.neucom.2021.10.120_b0075 article-title: Accuracy updated ensemble for data streams with concept drift – volume: 37 start-page: 132 year: 2017 ident: 10.1016/j.neucom.2021.10.120_b0010 article-title: Ensemble learning for data stream analysis: A survey publication-title: Inform. Fusion doi: 10.1016/j.inffus.2017.02.004 – start-page: 377 year: 2001 ident: 10.1016/j.neucom.2021.10.120_b0055 article-title: A streaming ensemble algorithm (sea) for large-scale classification – ident: 10.1016/j.neucom.2021.10.120_b0030 – volume: 11 start-page: 1601 year: 2010 ident: 10.1016/j.neucom.2021.10.120_b0040 article-title: MOA: massive online analysis publication-title: J. Mach. Learn. Res. – ident: 10.1016/j.neucom.2021.10.120_b0070 doi: 10.1145/956750.956778 – year: 2015 ident: 10.1016/j.neucom.2021.10.120_b0110 – ident: 10.1016/j.neucom.2021.10.120_b0165 doi: 10.1109/IJCNN48605.2020.9207498 – ident: 10.1016/j.neucom.2021.10.120_b0120 – volume: 12 start-page: 2825 year: 2011 ident: 10.1016/j.neucom.2021.10.120_b0020 article-title: Scikit-learn: Machine learning in Python publication-title: J. Mach. Learn. Res. – ident: 10.1016/j.neucom.2021.10.120_b0145 doi: 10.1016/j.neucom.2018.05.130 – ident: 10.1016/j.neucom.2021.10.120_b0130 doi: 10.1201/EBK1439826119-c1 – volume: 90 start-page: 317 issue: 3 year: 2013 ident: 10.1016/j.neucom.2021.10.120_b0135 article-title: On evaluating stream learning algorithms publication-title: Machine Learning doi: 10.1007/s10994-012-5320-9 – year: 1999 ident: 10.1016/j.neucom.2021.10.120_b0095 – ident: 10.1016/j.neucom.2021.10.120_b0085 doi: 10.1016/j.ins.2018.06.020 – volume: 27 start-page: 1356 issue: 5 year: 2015 ident: 10.1016/j.neucom.2021.10.120_b0065 article-title: Resampling-based ensemble methods for online class imbalance learning publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2014.2345380 |
| SSID | ssj0017129 |
| Score | 2.480275 |
| Snippet | Stream-learn is a Python package compatible with scikit-learn and developed for the drifting and imbalanced data stream analysis. Its main component is a... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 11 |
| SubjectTerms | Concept drift Data stream Dynamic class imbalance Imbalanced data |
| Title | Stream-learn — open-source Python library for difficult data stream batch analysis |
| URI | https://dx.doi.org/10.1016/j.neucom.2021.10.120 |
| Volume | 478 |
| WOSCitedRecordID | wos000761815800002&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-8286 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017129 issn: 0925-2312 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LaxsxEBYl6aGXvkvTFzr0tsistFprdTQlpe0hBOqCb4teBod4G2KnTW79Ef2F_SWdkbTrhYS-oJfFFl5baD59Hs3OzEfI63qqG85NybwIDRxQpoJpiW_rUNrKcmtKF8Um1NFRs1jo45w2tolyAqrrmstLffZfTQ1jYGwsnf0Lcw9fCgPwGowOVzA7XP_I8Pic2axZlIMo-lwGWaBMFkuh-uL4CjsGFDmCEzMNUSgltuEoMGc0lpCYdWGBqLH0LTUuGTuysamHi5IQOdgwW2PPBY8A2yVwb4A5VuHryqU49WSIU1_ZU6DXpJmdh3PsAY6tmMi2iz1eL4pJkUVRM3AbE8mGxKuNErFifUy8Mon3ZOrMnBsyj95I7ynScDLpwgXm-sCU-CR2yCh3f2dDkuFHnAjOQ2A5EseK8H2hag30vT97f7j4MDxtUlyknox54n2JZcwDvP5bN7swI7dkfp_czecJOks4eEBuhe4huddrddBM3Y_IfAwL-uPbdzoCBE2AoBkQFABBB0BQBARNgKARELQHxGPy6e3h_M07lhU1mIOj4ZbBxgsYwlIl91qIZePBgfbK8zLUwk-NVU6X3sO4lNZJsxSwXY2unIFjKJe-ekL2us9deEqoFdhaUFhX20YKpaw2QQZbBauXzXLKD0jVL1Lrcrt5VD05bfu8wpM2LW2LSxtHRXlA2HDXWWq38pvPq3792-wyJlewBcj88s5n_3znc3JntxdekL3t-UV4SW67L9vV5vxVxtZPKduSRA |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Stream-learn+%E2%80%94+open-source+Python+library+for+difficult+data+stream+batch+analysis&rft.jtitle=Neurocomputing+%28Amsterdam%29&rft.au=Ksieniewicz%2C+P.&rft.au=Zyblewski%2C+P.&rft.date=2022-03-14&rft.pub=Elsevier+B.V&rft.issn=0925-2312&rft.eissn=1872-8286&rft.volume=478&rft.spage=11&rft.epage=21&rft_id=info:doi/10.1016%2Fj.neucom.2021.10.120&rft.externalDocID=S0925231222000108 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0925-2312&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0925-2312&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0925-2312&client=summon |