Selecting Representative Data Sets
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The aim of the chapter is to give an exhaustive overview and comparision of existing methods that deal with the methods of data selection and sampling. A general approach to the problem of optimal data selection (we could call it also splitting, dividing, sampling, ...) to training, testing and eventually validation sets is discussed. An overview of the methods together with their features, utilization, positives and negatives is given. Aptly presented algorithms of the methods are clearly summarized. Principles of selected methods are visualized in pictures and charts.
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There are several popular synthetic datasets like SYNTHIA [44] and CARLA [13].
“There are several popular synthetic datasets like SYNTHIA [44] and CARLA [13].”
From WoodScape: A Multi-Task, Multi-Camera Fisheye Dataset for Autonomous Driving (2019) · cites this paper as [44]
The citation is attached to SYNTHIA, but the passages from “Selecting Representative Data Sets” (page unavailable) discuss representative-set selection and SMOTE synthetic instances, not SYNTHIA. Searches for SYNTHIA and CARLA found no passage identifying either dataset or addressing its popularity. (LLM, conf=0.76)
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References 117 · Cited by 94
Research works printed but not linked: 6
- Cover, T. & Hart, P. [1967]. Nearest neighbor pattern classification, Information Theory, IEEE Transactions on 13(1): 21-27.
- Devijver, P. & Kittler, J. [1980]. On the edited nearest neighbor rule, Proc. 5th Int. Conf. on Pattern Recognition, pp. 72-80.
- Domingos, P. [1999]. Metacost: A general method for making classifiers cost-sensitive, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp. 155-164.
- Han, J. & Kamber, M. [2006]. Data mining: concepts and techniques, The Morgan Kaufmann series in data management systems, Elsevier. URL: http://books.google.cz/books?id=AfL0t-YzOrEC
- Jankowski, N. & Grochowski, M. [2004b]. Comparison of instances seletion algorithms i. algorithms survey, Artificial Intelligence and Soft Computing-ICAISC 2004 pp. 598-603.
- Vázquez, F., Sánchez, J. & Pla, F. [2005]. A stochastic approach to wilsonâ Ȃ Źs editing algorithm, Pattern Recognition and Image Analysis pp. 35-42.
- Authors
- Borovicka, T., Jirina, M., Kordik, P., & Jiri, M.
- Affiliations
- Czech Academy of Sciences (CZ)
- Venue
- Advances in Data Mining Knowledge Discovery and Applications
- Publisher
- InTech
- Published
- 2012-09-12
- OpenAlex
- W1918087345
- Cited by
- 94
- References
- 117
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 11 · 2023: 6 · 2022: 13 · 2021: 11 · 2020: 11 · 2019: 6 · 2018: 7 · 2017: 7 · 2016: 4 · 2015: 4 · 2013: 6
- FWCI
- 5.68
- Type
- book chapter
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- InTech eBooks (publishedVersion, cc-by)
- ASEP (submittedVersion, public-domain)
- BiblioBoard Library Catalog (Open Research Library) (submittedVersion, cc-by)
- Contact-less Assessment of In-vivo Body Signals Using Microwave Doppler Radar (InTech) (submittedVersion, cc-by)
- Funders
- Akademie Věd České Republiky (RVO: 67985807, 67985807)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Imbalanced Data Classification Techniques
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- data selection, sampling, training data selection, test set selection, data partitioning, machine learning data preparation
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