Feature engineering (English Wikipedia)

Lim, Lek-Heng; Comon, Pierre (2009-04-12). "Nonnegative approximations of nonnegative tensors". arXiv:0903.4530 [cs.NA].
Thanh Lam, Hoang; Thiebaut, Johann-Michael; Sinn, Mathieu; Chen, Bei; Mai, Tiep; Alkan, Oznur (2017-06-01). "One button machine for automating feature engineering in relational databases". arXiv:1706.00327 [cs.DB].
Van Der Donckt, Jonas; Van Der Donckt, Jeroen; Deprost, Emiel; Van Hoecke, Sofie (2022). "tsflex: Flexible time series processing & feature extraction". SoftwareX. 17: 100971. arXiv:2111.12429. Bibcode:2022SoftX..1700971V. doi:10.1016/j.softx.2021.100971. S2CID 244527198. Retrieved September 7, 2022.
Bengio, Yoshua (2012), "Practical Recommendations for Gradient-Based Training of Deep Architectures", Neural Networks: Tricks of the Trade, Lecture Notes in Computer Science, vol. 7700, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 437–478, arXiv:1206.5533, doi:10.1007/978-3-642-35289-8_26, ISBN 978-3-642-35288-1, S2CID 10808461, retrieved 2023-03-21

berkeley.edu

cs.berkeley.edu

"Feature engineering and selection" (PDF). Alexandre Bouchard-Côté. October 1, 2009. Retrieved 12 November 2015.

books.google.com

Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer. ISBN 978-0-387-84884-6.

cuni.cz

ufal.mff.cuni.cz

"Feature engineering in Machine Learning" (PDF). Zdenek Zabokrtsky. Archived from the original (PDF) on 4 March 2016. Retrieved 12 November 2015.

dmbee.github.io

"seglearn user guide". Retrieved September 7, 2022.

doi.org

Sharma, Shubham; Nayak, Richi; Bhaskar, Ashish (2024-05-01). "Multi-view feature engineering for day-to-day joint clustering of multiple traffic datasets". Transportation Research Part C: Emerging Technologies. 162: 104607. Bibcode:2024TRPC..16204607S. doi:10.1016/j.trc.2024.104607. ISSN 0968-090X.
MacQueron C (2021). SOLID-LIQUID MIXING IN STIRRED TANKS : Modeling, Validation, Design Optimization and Suspension Quality Prediction (Report). doi:10.13140/RG.2.2.11074.84164/1.
Lee, Daniel D.; Seung, H. Sebastian (1999). "Learning the parts of objects by non-negative matrix factorization". Nature. 401 (6755): 788–791. Bibcode:1999Natur.401..788L. doi:10.1038/44565. ISSN 1476-4687. PMID 10548103.
Wang, Hua; Nie, Feiping; Huang, Heng; Ding, Chris (2011). "Nonnegative Matrix Tri-factorization Based High-Order Co-clustering and Its Fast Implementation". 2011 IEEE 11th International Conference on Data Mining. IEEE. pp. 774–783. doi:10.1109/icdm.2011.109. ISBN 978-1-4577-2075-8.
Nayak, Richi; Luong, Khanh (2023). "Multi-aspect Learning". Intelligent Systems Reference Library. 242. doi:10.1007/978-3-031-33560-0. ISBN 978-3-031-33559-4. ISSN 1868-4394.
Knobbe AJ, Siebes A, Van Der Wallen D (1999). "Multi-relational Decision Tree Induction" (PDF). Principles of Data Mining and Knowledge Discovery. Lecture Notes in Computer Science. Vol. 1704. pp. 378–383. doi:10.1007/978-3-540-48247-5_46. ISBN 978-3-540-66490-1.
Yin X, Han J, Yang J, Yu PS (2004). "CrossMine: Efficient classification across multiple database relations". Proceedings. 20th International Conference on Data Engineering. pp. 399–410. doi:10.1109/ICDE.2004.1320014. ISBN 0-7695-2065-0. S2CID 1183403.
Frank R, Moser F, Ester M (2007). "A Method for Multi-relational Classification Using Single and Multi-feature Aggregation Functions". Knowledge Discovery in Databases: PKDD 2007. Lecture Notes in Computer Science. Vol. 4702. pp. 430–437. doi:10.1007/978-3-540-74976-9_43. ISBN 978-3-540-74975-2.
Van Der Donckt, Jonas; Van Der Donckt, Jeroen; Deprost, Emiel; Van Hoecke, Sofie (2022). "tsflex: Flexible time series processing & feature extraction". SoftwareX. 17: 100971. arXiv:2111.12429. Bibcode:2022SoftX..1700971V. doi:10.1016/j.softx.2021.100971. S2CID 244527198. Retrieved September 7, 2022.
Kanter, James Max; Veeramachaneni, Kalyan (2015). "Deep feature synthesis: Towards automating data science endeavors". 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). pp. 1–10. doi:10.1109/DSAA.2015.7344858. ISBN 978-1-4673-8272-4. S2CID 206610380.
Sarker IH (November 2021). "Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions". SN Computer Science. 2 (6): 420. doi:10.1007/s42979-021-00815-1. PMC 8372231. PMID 34426802.
Bengio, Yoshua (2012), "Practical Recommendations for Gradient-Based Training of Deep Architectures", Neural Networks: Tricks of the Trade, Lecture Notes in Computer Science, vol. 7700, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 437–478, arXiv:1206.5533, doi:10.1007/978-3-642-35289-8_26, ISBN 978-3-642-35288-1, S2CID 10808461, retrieved 2023-03-21

dx.doi.org

Wang, Hua; Nie, Feiping; Huang, Heng; Ding, Chris (2011). "Nonnegative Matrix Tri-factorization Based High-Order Co-clustering and Its Fast Implementation". 2011 IEEE 11th International Conference on Data Mining. IEEE. pp. 774–783. doi:10.1109/icdm.2011.109. ISBN 978-1-4577-2075-8.
Bengio, Yoshua (2012), "Practical Recommendations for Gradient-Based Training of Deep Architectures", Neural Networks: Tricks of the Trade, Lecture Notes in Computer Science, vol. 7700, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 437–478, arXiv:1206.5533, doi:10.1007/978-3-642-35289-8_26, ISBN 978-3-642-35288-1, S2CID 10808461, retrieved 2023-03-21

explorium.ai

explorium_admin (2021-10-25). "5 Reasons Why Feature Engineering is Challenging". Explorium. Retrieved 2023-03-21.

featurestore.org

"What is a feature store". Retrieved 2022-04-19.

featuretools.com

"Featuretools - An open source python framework for automated feature engineering". Retrieved September 7, 2022.

getml.com

docs.getml.com

"getML documentation". Retrieved September 7, 2022.

github.com

"github: alteryx/featuretools". GitHub. Retrieved September 7, 2022.
Sharma, Shubham, mcmd: Multi-view Classification framework based on Consensus Matrix Decomposition developed by Shubham Sharma at QUT, retrieved 2024-04-14
"github: getml/getml-community". GitHub. Retrieved September 7, 2022.
"predict-idlab/tsflex". GitHub. Retrieved September 7, 2022.
"github: facebookresearch/Kats". GitHub. Retrieved September 7, 2022.

harvard.edu

ui.adsabs.harvard.edu

Sharma, Shubham; Nayak, Richi; Bhaskar, Ashish (2024-05-01). "Multi-view feature engineering for day-to-day joint clustering of multiple traffic datasets". Transportation Research Part C: Emerging Technologies. 162: 104607. Bibcode:2024TRPC..16204607S. doi:10.1016/j.trc.2024.104607. ISSN 0968-090X.
Lee, Daniel D.; Seung, H. Sebastian (1999). "Learning the parts of objects by non-negative matrix factorization". Nature. 401 (6755): 788–791. Bibcode:1999Natur.401..788L. doi:10.1038/44565. ISSN 1476-4687. PMID 10548103.
Van Der Donckt, Jonas; Van Der Donckt, Jeroen; Deprost, Emiel; Van Hoecke, Sofie (2022). "tsflex: Flexible time series processing & feature extraction". SoftwareX. 17: 100971. arXiv:2111.12429. Bibcode:2022SoftX..1700971V. doi:10.1016/j.softx.2021.100971. S2CID 244527198. Retrieved September 7, 2022.

mit.edu

news.mit.edu

"Automating big-data analysis". 16 October 2015.

nature.com

Lee, Daniel D.; Seung, H. Sebastian (1999). "Learning the parts of objects by non-negative matrix factorization". Nature. 401 (6755): 788–791. Bibcode:1999Natur.401..788L. doi:10.1038/44565. ISSN 1476-4687. PMID 10548103.

nih.gov

pubmed.ncbi.nlm.nih.gov

Lee, Daniel D.; Seung, H. Sebastian (1999). "Learning the parts of objects by non-negative matrix factorization". Nature. 401 (6755): 788–791. Bibcode:1999Natur.401..788L. doi:10.1038/44565. ISSN 1476-4687. PMID 10548103.
Sarker IH (November 2021). "Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions". SN Computer Science. 2 (6): 420. doi:10.1007/s42979-021-00815-1. PMC 8372231. PMID 34426802.

ncbi.nlm.nih.gov

Sarker IH (November 2021). "Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions". SN Computer Science. 2 (6): 420. doi:10.1007/s42979-021-00815-1. PMC 8372231. PMID 34426802.

phaseai.com

"An Introduction to Feature Stores". Retrieved 2021-04-15.

princeton.edu

cs.princeton.edu

"Feature Engineering" (PDF). 2010-04-22. Retrieved 12 November 2015.

reality.ai

"Its all about the features". Reality AI Blog. September 2017.

researchgate.net

MacQueron C (2021). SOLID-LIQUID MIXING IN STIRRED TANKS : Modeling, Validation, Design Optimization and Suspension Quality Prediction (Report). doi:10.13140/RG.2.2.11074.84164/1.
"Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)". Retrieved September 7, 2022.

sciencedirect.com

Van Der Donckt, Jonas; Van Der Donckt, Jeroen; Deprost, Emiel; Van Hoecke, Sofie (2022). "tsflex: Flexible time series processing & feature extraction". SoftwareX. 17: 100971. arXiv:2111.12429. Bibcode:2022SoftX..1700971V. doi:10.1016/j.softx.2021.100971. S2CID 244527198. Retrieved September 7, 2022.

section.io

"Feature Engineering in Machine Learning". Engineering Education (EngEd) Program | Section. Retrieved 2023-03-21.

semanticscholar.org

api.semanticscholar.org

Yin X, Han J, Yang J, Yu PS (2004). "CrossMine: Efficient classification across multiple database relations". Proceedings. 20th International Conference on Data Engineering. pp. 399–410. doi:10.1109/ICDE.2004.1320014. ISBN 0-7695-2065-0. S2CID 1183403.
Van Der Donckt, Jonas; Van Der Donckt, Jeroen; Deprost, Emiel; Van Hoecke, Sofie (2022). "tsflex: Flexible time series processing & feature extraction". SoftwareX. 17: 100971. arXiv:2111.12429. Bibcode:2022SoftX..1700971V. doi:10.1016/j.softx.2021.100971. S2CID 244527198. Retrieved September 7, 2022.
Kanter, James Max; Veeramachaneni, Kalyan (2015). "Deep feature synthesis: Towards automating data science endeavors". 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). pp. 1–10. doi:10.1109/DSAA.2015.7344858. ISBN 978-1-4673-8272-4. S2CID 206610380.
Bengio, Yoshua (2012), "Practical Recommendations for Gradient-Based Training of Deep Architectures", Neural Networks: Tricks of the Trade, Lecture Notes in Computer Science, vol. 7700, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 437–478, arXiv:1206.5533, doi:10.1007/978-3-642-35289-8_26, ISBN 978-3-642-35288-1, S2CID 10808461, retrieved 2023-03-21

springer.com

link.springer.com

Knobbe AJ, Siebes A, Van Der Wallen D (1999). "Multi-relational Decision Tree Induction" (PDF). Principles of Data Mining and Knowledge Discovery. Lecture Notes in Computer Science. Vol. 1704. pp. 378–383. doi:10.1007/978-3-540-48247-5_46. ISBN 978-3-540-66490-1.

tsfel.readthedocs.io

"Welcome to TSFEL documentation!". Retrieved September 7, 2022.

tsfresh.readthedocs.io

"tsfresh documentation". Retrieved September 7, 2022.

web.archive.org

"Feature engineering in Machine Learning" (PDF). Zdenek Zabokrtsky. Archived from the original (PDF) on 4 March 2016. Retrieved 12 November 2015.

worldcat.org

search.worldcat.org

Sharma, Shubham; Nayak, Richi; Bhaskar, Ashish (2024-05-01). "Multi-view feature engineering for day-to-day joint clustering of multiple traffic datasets". Transportation Research Part C: Emerging Technologies. 162: 104607. Bibcode:2024TRPC..16204607S. doi:10.1016/j.trc.2024.104607. ISSN 0968-090X.
Lee, Daniel D.; Seung, H. Sebastian (1999). "Learning the parts of objects by non-negative matrix factorization". Nature. 401 (6755): 788–791. Bibcode:1999Natur.401..788L. doi:10.1038/44565. ISSN 1476-4687. PMID 10548103.
Nayak, Richi; Luong, Khanh (2023). "Multi-aspect Learning". Intelligent Systems Reference Library. 242. doi:10.1007/978-3-031-33560-0. ISBN 978-3-031-33559-4. ISSN 1868-4394.
Spiegelhalter, D. J. (2019). The art of statistics : learning from data. [London] UK. ISBN 978-0-241-39863-0. OCLC 1064776283.{{cite book}}: CS1 maint: location missing publisher (link)

worldcat.org

Spiegelhalter, D. J. (2019). The art of statistics : learning from data. [London] UK. ISBN 978-0-241-39863-0. OCLC 1064776283.{{cite book}}: CS1 maint: location missing publisher (link)