The DOI matches its Crossref record; 13 of the 14 references it cites were found in a registry or an index.
A survey of machine learning techniques for food sales prediction
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Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“ML may be able to mitigate these issues of overproducing and/or overstocking goods by improving demand forecasting [12,797].”
From Tackling climate change with machine learning (2023) · cites this paper as [797]
Citations
Cites
A few useful things to know about machine learning
Domingos, P. (2012) · Communications of the ACM · doi:10.1145/2347736.2347755 · Cited by 3,326
Landwehr, N., Hall, M., & Frank, E. (2005) · Machine Learning · doi:10.1007/s10994-005-0466-3 · Cited by 1,218
A Simple Approach to Ordinal Classification
Frank, E., & Hall, M. (2001) · Lecture Notes in Computer Science · doi:10.1007/3-540-44795-4_13 · Cited by 606
Doganis, P., Alexandridis, A., Patrinos, P., & Sarimveis, H. (2005) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2005.03.056 · Cited by 196
Dynamic integration of classifiers for handling concept drift
Tsymbal, A., Pechenizkiy, M., Cunningham, P., & Puuronen, S. J. (2007) · Information Fusion · doi:10.1016/j.inffus.2006.11.002 · Cited by 195
Supply Chain Management in Food Chains: Improving Performance by Reducing Uncertainty
van der Vorst, J. G. A. J., Beulens, A. J. M., de Wit, W., & van Beek, P. (1998) · International Transactions in Operational Research · doi:10.1111/j.1475-3995.1998.tb00131.x · Cited by 192
Multi-target regression with rule ensembles
Aho, T., Ženko, B., Džeroski, S., & Elomaa, T. (2012) · Journal of Machine Learning Research · Cited by 92
The study of a forecasting sales model for fresh food
Chen, C.-Y., Lee, W., Kuo, H., Chen, C., & Chen, K.-H. (2010) · Expert Systems with Applications · doi:10.1016/j.eswa.2010.04.072 · Cited by 88
Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?
Žliobaitė, I., Bakker, J., & Pechenizkiy, M. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.07.078 · Cited by 40
Food Sales Prediction with Meteorological Data — A Case Study of a Japanese Chain Supermarket
Liu, X., & Ichise, R. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-61845-6_10 · Cited by 29
Food Sales Prediction: "If Only It Knew What We Know"
Meulstee, P., & Pechenizkiy, M. (2008) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2008.128 · Cited by 23
Towards Context Aware Food Sales Prediction
liobaite, I., Bakker, J., & Pechenizkiy, M. (2009) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2009.60 · Cited by 18
Food Wholesales Prediction: What Is Your Baseline?
Bakker, J., & Pechenizkiy, M. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04125-9_52 · Cited by 7
Cited by
Sorted by citations
The fourth industrial revolution in the food industry—Part I: Industry 4.0 technologies
Hassoun, A., Aït‐Kaddour, A., Abu-Mahfouz, A. M. I., Rathod, N. B., Bader, F., Barba, F. J., Biancolillo, A., Cropotova, J., Galanakis, C. M., Jambrak, A. ., et al. (2022) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2022.2034735 · Cited by 453
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336
Application of Artificial Intelligence in Food Industry—a Guideline
Mavani, N. R., Ali, J. M., Othman, S., Hussain, M. A., Hashim, H., & Rahman, N. A. (2021) · Food Engineering Reviews · doi:10.1007/s12393-021-09290-z · Cited by 361
A survey on hyperparameters optimization algorithms of forecasting models in smart grid
Khalid, R., & Javaid, N. (2020) · Sustainable Cities and Society · doi:10.1016/j.scs.2020.102275 · Cited by 194
The Role of Machine Learning in Tribology: A Systematic Review
Paturi, U. M. R., Palakurthy, S. T., & Reddy, N. S. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09841-5 · Cited by 145
Metadata
- Authors
- Tsoumakas, G.
- Affiliations
- Aristotle University of Thessaloniki (GR)
- Corresponding author
- Tsoumakas, Grigorios
- ORCID
- Tsoumakas, Grigorios
- Venue
- Artificial Intelligence Review
- Published
- 2018-06-14
- Volume
- 52
- Issue
- 1
- Pages
- 441-447
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W2808163457
- Cited by
- 134
- References
- 14
- Citations by year
- 2026: 3 · 2025: 15 · 2024: 34 · 2023: 31 · 2022: 21 · 2021: 19 · 2020: 8 · 2019: 3
- FWCI
- 5.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Artificial Intelligence Review (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- supermarket inventory management, stockouts, feature selection, forecasting accuracy evaluation, food industry
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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