Evaluating the effect of dataset size on predictive model using supervised learning technique
Open accessAbstract
Learning models used for prediction purposes are mostly developed without paying much cognizance to the size of datasets that can produce models of high accuracy and better generalization.Although, the general believe is that, large dataset is needed to construct a predictive learning model.To describe a data set as large in size, perhaps, is circumstance dependent, thus, what constitutes a dataset to be considered as being big or small is vague.In this paper, the ability of the predictive model to generalize with respect to a particular size of data when simulated with new untrained input is examined.The study experiments on three different sizes of data using Matlab program to create predictive models with a view to establishing if the size of data has any effect on the accuracy of a model.The simulated output of each model is measured using the Mean Absolute Error (MAE) and comparisons are made.Findings from this study reveals that, the quantity of data partitioned for the purpose of training must be of good representation of the entire sets and sufficient enough to span through the input space.The results of simulating the three network models also shows that, the learning model with the largest size of training sets appears to be the most accurate and consistently delivers a much better and stable results.
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Several sources state that a dataset of 1000 images per class is required to successfully train a neural network.
“Several sources state that a dataset of 1000 images per class is required to successfully train a neural network [74,78-80].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [79]
The Experimental Results (page unavailable) compare neural-network models built with 400, 800, and 1200 data points, and the Conclusions say that sufficient data can improve accuracy. The paper does not discuss images per class or state that 1000 per class is required for successful training; it reports that all three models trained well. (LLM, conf=0.84)
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Cites
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,655 · see citing works
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works
Neural Networks And Learning Machines
Haykin, S. (2010) · Cited by 6,611 · see citing works
Rajaraman, A., & Ullman, J. D. (2011) · Cambridge University Press eBooks · doi:10.1017/cbo9781139058452 · Cited by 2,287 · see citing works
Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints
van der Ploeg, T., Austin, P. C., & Steyerberg, E. W. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-137 · Cited by 809 · see citing works
Özel, T., & Karpat, Y. (2004) · International Journal of Machine Tools and Manufacture · doi:10.1016/j.ijmachtools.2004.09.007 · Cited by 702 · see citing works
Bidgoli, B. M., Kashy, D. A., Kortemeyer, G., & Punch, W. F. (2004) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2003.1263284 · Cited by 334 · see citing works
Estimating Dataset Size Requirements for Classifying DNA Microarray Data
Mukherjee, S., Tamayo, P., Rogers, S., Rifkin, R. M., Engle, A., Campbell, C., Golub, T. R., & Mesirov, J. P. (2003) · Journal of Computational Biology · doi:10.1089/106652703321825928 · Cited by 279 · see citing works
Data mining approach for predicting student performance
Osmanbegović, E., & Suljić, M. (2012) · Economic Review · doi:10.5281/zenodo.15695548 · Cited by 242 · see citing works
DATA MINING APPROACH FOR PREDICTING STUDENT PERFORMANCE
Suljić, M., & Osmanbegović, E. (2012) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 199 · see citing works
Oladokun, V. O., Adebanjo, A. S., & Charles-Owaba, O. E. (2008) · Cited by 181 · see citing works
Sample size planning for developing classifiers using high-dimensional DNA microarray data
Dobbin, K. K., & Simon, R. M. (2006) · Biostatistics · doi:10.1093/biostatistics/kxj036 · Cited by 133 · see citing works
Runoff and Sediment Yield Modeling Using Artificial Neural Networks: Upper Siwane River, India
Raghuwanshi, N. S., Singh, R. P., & Reddy, L. S. (2005) · Journal of Hydrologic Engineering · doi:10.1061/(asce)1084-0699(2006)11:1(71 · Cited by 121 · see citing works
Understanding Complex Datasets: Data Mining with Matrix Decompositions
Skillicorn, D. B. (2007) · Cited by 117 · see citing works
Bandyopadhyay, G., & Chattopadhyay, S. (2007) · International Journal of Environmental Science and Technology · doi:10.1007/bf03325972 · Cited by 107 · see citing works
Bidgoli, B. M., Kashy, D. A., Kortemeyer, G., & Punch, W. F. (2003) · Cited by 80 · see citing works
Practical applications of data mining
(2011) · Choice Reviews Online · doi:10.5860/choice.49-0327 · Cited by 53 · see citing works
Schumacher, P. A., Olinsky, A. D., Quinn, J., & Smith, R. M. (2010) · Journal of Education for Business · doi:10.1080/08832320903449477 · Cited by 30 · see citing works
Basavanhally, A. N., Doyle, S. T., & Madabhushi, A. (2010) · IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI) · doi:10.1109/isbi.2010.5490373 · Cited by 15 · see citing works
Predicting with a small amount of data: an application of fuzzy reasoning to regional disparities
McArthur, D. P., Encheva, S., & Thorsen, I. (2014) · Journal of Economic Studies · doi:10.1108/jes-06-2012-0077 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Khalil, M., McGough, A. S., Pourmirza, Z., Pazhoohesh, M., & Walker, S. L. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105287 · Cited by 307 · see citing works
Data-driven personal thermal comfort prediction: A literature review
Feng, Y., Liu, S., Wang, J., Yang, J., Jao, Y., & Wang, N. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112357 · Cited by 117 · see citing works
Ogami, C., Tsuji, Y., Seki, H., Kawano, H., To, H., Matsumoto, Y., & Hosono, H. (2021) · CPT Pharmacometrics & Systems Pharmacology · doi:10.1002/psp4.12643 · Cited by 81 · see citing works
Bhuiyan, M. A. E., Yang, F., Biswas, N. K., Rahat, S. H., & Neelam, T. J. (2020) · Forecasting · doi:10.3390/forecast2030014 · Cited by 79 · see citing works
Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection
Aust, J., Shankland, S., Pons, D., Mukundan, R., & Mitrovic, A. (2021) · Aerospace · doi:10.3390/aerospace8020030 · Cited by 66 · see citing works
Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications
Dumakor-Dupey, N. K., Arya, S., & Jha, A. (2021) · Minerals · doi:10.3390/min11060601 · Cited by 64 · see citing works
Aust, J., & Pons, D. J. (2022) · Applied Sciences · doi:10.3390/app12042250 · Cited by 58 · see citing works
Vaughan, L., Zhang, M., Gu, H., Rose, J. B., Naughton, C. C., Medema, G. J., Allan, V., Roiko, A. H., Blackall, L. L., & Zamyadi, A. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.159748 · Cited by 54 · see citing works
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Khairy, M., Mahmoud, T. M., & El‐Hafeez, T. A. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-09084-w · Cited by 53 · see citing works
Gogoi, P., & Valan, J. A. (2024) · International Urology and Nephrology · doi:10.1007/s11255-024-04281-5 · Cited by 53 · see citing works
Rusdi, N. ’., Kasihmuddin, M. S. M., Romli, N. A., Manoharam, G., & Mansor, M. A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101554 · Cited by 52 · see citing works
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Nikfar, M., Bitencourt, J., & Mykoniatis, K. (2022) · Procedia Computer Science · doi:10.1016/j.procs.2022.01.210 · Cited by 49 · see citing works
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Ayyıldız, N., & İskenderoğlu, Ö. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e24123 · Cited by 44 · see citing works
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Rahman, A., Marufuzzaman, M., Street, J., Wooten, J. R., Gude, V. G., Buchanan, R., & Wang, H. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113843 · Cited by 39 · see citing works
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Raja, N. B., Lauchstedt, A., Pandolfi, J. M., Kim, S. W., Budd, A. F., & Kiessling, W. (2021) · Global Ecology and Biogeography · doi:10.1111/geb.13321 · Cited by 36 · see citing works
Garcia‐Vozmediano, A., Maurella, C., Ceballos, L. A., Crescio, E., Meo, R., Martelli, W., Pitti, M., Lombardi, D., Meloni, D., Pasqualini, C., et al. (2024) · Veterinary Research · doi:10.1186/s13567-024-01323-9 · Cited by 34 · see citing works
Chen, X., Shuai, C., Zhao, B., Zhang, Y., & Li, K. (2023) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2023.107050 · Cited by 32 · see citing works
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Lerebourg, L., Saboul, D., Clémençon, M., & Coquart, J. B. (2022) · International Journal of Sports Medicine · doi:10.1055/a-1993-2371 · Cited by 29 · see citing works
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Przybylak, K. R., Madden, J. C., Covey‐Crump, E. M., Gibson, L., Barber, C., Patel, M. L., & Cronin, M. (2017) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1080/17425255.2017.1316449 · Cited by 28 · see citing works
Li, F., Rana, M. S., & Qurashi, M. A. (2024) · Multiscale and Multidisciplinary Modeling Experiments and Design · doi:10.1007/s41939-024-00672-4 · Cited by 26 · see citing works
Wahab, S., Suleiman, M. F., Shabbir, F., Mahmoudabadi, N. S., Waqas, S., Herl, N., & Ahmad, A. (2024) · Journal of Civil Engineering Frontiers · doi:10.38094/jocef40271 · Cited by 25 · see citing works
Alatefi, S., Agwu, O. E., Amar, M. N., & Alkouh, A. B. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107304 · Cited by 24 · see citing works
Attai, K. F., Amannejad, Y., Pour, M. V., Obot, O. U., & Uzoka, F. (2022) · Tropical Medicine and Infectious Disease · doi:10.3390/tropicalmed7120398 · Cited by 24 · see citing works
Alatefi, S., Agwu, O. E., Amar, M. N., & Djema, H. (2025) · Fuel · doi:10.1016/j.fuel.2025.135073 · Cited by 23 · see citing works
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Agwu, O. E., Alatefi, S., Azim, R. R. A., & Alkouh, A. B. (2024) · Flow Measurement and Instrumentation · doi:10.1016/j.flowmeasinst.2024.102613 · Cited by 23 · see citing works
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Lin, P., Zhang, L., & Zuo, J. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109616 · Cited by 22 · see citing works
Niu, Y., Tye, Y. Y., Hemashini, T., & Leh, C. P. (2025) · Biomass and Bioenergy · doi:10.1016/j.biombioe.2025.107956 · Cited by 18 · see citing works
Saedi, B., & Mohammadi, S. D. (2021) · Rock Mechanics and Rock Engineering · doi:10.1007/s00603-021-02575-z · Cited by 18 · see citing works
Fernandes, P. H. E., Silva, G. C., Pitz, D. B., Schnelle, M., Koschek, K., Nagel, C., & Beber, V. C. (2023) · Applied Mechanics · doi:10.3390/applmech4010019 · Cited by 18 · see citing works
Khan, N., Nwafor-Okoli, C., Ekpin, V. I., Attai, K. F., Chukwudi, N., Sabi, H. M., Akwaowo, C., Osuji, J. C., Benavente, L., & Uzoka, F. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119638 · Cited by 18 · see citing works
Abbasi, N., Lacson, R. C., Kapoor, N., Licaros, A., Guenette, J. P., Burk, K. S., Hammer, M. M., Desai, S. P., Eappen, S., Saini, S., et al. (2023) · American Journal of Roentgenology · doi:10.2214/ajr.23.29120 · Cited by 18 · see citing works
Cherni, A., Zeiri, N., Yahyaoui, N., BAŞER, P., Said, M., Saadaoui, S., & Murshed, M. N. (2025) · Results in Physics · doi:10.1016/j.rinp.2025.108186 · Cited by 17 · see citing works
Zhou, Y., Dowsey, M. M., Spelman, T. D., Choong, P., & Schilling, C. G. (2023) · ANZ Journal of Surgery · doi:10.1111/ans.18250 · Cited by 16 · see citing works
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Wei, Z., Zhu, S., Chen, X., Zhu, C., Duan, B., & Liu, Q. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.09.006 · Cited by 14 · see citing works
Vidaček, S., Valinger, D., Mikac, L., Ivanda, M., Kljusurić, J. G., & Janči, T. (2021) · Foods · doi:10.3390/foods10081767 · Cited by 14 · see citing works
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Bandini, E., Kajtazi, A., Szücs, R., & Lynen, F. (2025) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2025.118207 · Cited by 12 · see citing works
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Izzo, F., & Picone, Q. (2022) · Journal of Tourism and Services · doi:10.29036/jots.v13i25.393 · Cited by 11 · see citing works
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Zhu, S., Zhang, M., Wang, C., Guo, J., Chen, X., & Xie, M. (2024) · Journal of Hydrologic Engineering · doi:10.1061/jhyeff.heeng-6091 · Cited by 8 · see citing works
Ströbel, R., Mau, M., Puchta, A., & Fleischer, J. (2024) · Machine Learning and Knowledge Extraction · doi:10.3390/make6020049 · Cited by 7 · see citing works
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Sutojo, T., Syukur, A., Rustad, S., Shidik, G. F., Santoso, H. A., Purwanto, P., & Muljono, M. (2020) · International Seminar on Application for Technology of Information and Communication (iSemantic) · doi:10.1109/isemantic50169.2020.9234265 · Cited by 5 · see citing works
Assegaf, B. (2017) · Journal of Telematics and Informatics · doi:10.12928/jti.v5i1.22-28 · Cited by 5 · see citing works
Álvarez, V. E., Quiroga, M. P., & Centrón, D. (2023) · mSystems · doi:10.1128/msystems.00734-22 · Cited by 4 · see citing works
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Krishnan, D., Jerin Mahibha, C., & Thenmozhi, D. (2022) · International Workshop on Semantic Evaluation (SemEval) · doi:10.18653/v1/2022.semeval-1.114 · Cited by 4 · see citing works
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Wadekar, S. A. (2023) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets56262.2023.10041324 · Cited by 1 · see citing works
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Mubarak, S., Islam, M. R., Balla, A., & Habaebi, M. H. (2023) · International Journal of Security and Networks · doi:10.1504/ijsn.2023.10058681 · Cited by 3 · see citing works
Indriani, L., Riyadi, S., & Zaki, A. (2024) · BIO Web of Conferences · doi:10.1051/bioconf/202414406002 · Cited by 1 · see citing works
CPI Index Prediction Based on SARIMA-LSTM-CatBoost Model
Liang, J., & Yanru, Z. (2024) · Asia-Pacific Artificial Intelligence and Big Data Forum (AIBDF) · doi:10.1145/3718491.3718665 · Cited by 1 · see citing works
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Дмитриевич, Е. С., & Zhuravlev, A. P. (2020) · 2018Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO) · doi:10.1109/synchroinfo49631.2020.9166001 · Cited by 1 · see citing works
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Pakpahan, J. A., Panjaitan, Y. C., Amalia, J., & Pakpahan, M. B. (2022) · JATISI (Jurnal Teknik Informatika dan Sistem Informasi) · doi:10.35957/jatisi.v9i4.1332 · Cited by 1 · see citing works
(2019) · i-manager’s Journal on English Language Teaching · doi:10.26634/jelt.9.3.15653
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Li, Y., Liu, R., Zhang, Z., Ding, N., Shen, Y., Tao, L., & Zheng, H.-T. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.08470
Sanjay, M., Gaurav, K., González‐Pabón, M. J., Fuchs, J., Mikkelsen, S. R., & Cortón, E. (2021) · PLoS ONE · doi:10.1371/journal.pone.0248159
Álvarez, V. E., Quiroga, M. P., & Centrón, D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.18.464923
Makola, T., Mulaudzi, R., & Ajoodha, R. (2021) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet52533.2021.9698753
Peramalan Jumlah Kasus COVID-19 Menggunakan Joint Learning
Nur, M. R., Amin, F. M., & Yusuf, A. (2022) · Jurnal Teknik Informatika dan Sistem Informasi · doi:10.28932/jutisi.v8i1.4469
Dynamic Light Settings as Data Augmentations for Automated Scratch Detection
Valentin, G., Fukuda, O., Yeoh, W. L., Hiroshi, O., & Nobuhiko, Y. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.01312107
การพฒนาโมเดลการวเคราะหอารมณครในชนเรยนผานการรจำคำพดโดยใชการเรยนรเชงลก
รุจิกรหิรัณย์, จ. (2022) · doi:10.58837/chula.the.2022.789
PrivateCTGAN: Adapting GAN for Privacy-Aware Tabular Data Sharing
Lopes, F., Soares, C., & Cortez, P. C. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-74627-7_12
Research for SARIMA and PatchTSMixer Models on the IEA Monthly Statistics Dataset
Hu, Y. (2025) · ITM Web of Conferences · doi:10.1051/itmconf/20257003011
Peter, I., Anderson, E. J., Siegfried, M. R., & Kurtz, N. T. (2025) · Earth and Space Science · doi:10.1029/2024ea004155
Development of a Framework for Generative AI-Based Safety Management via Prompt Engineering
Park, S., Jung, K., Kim, N., & Jeon, J. (2025) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784486139.078
Yu, X., Zhou, Y., Zheng, P., Wang, W., & Liao, S. (2026) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-026-01886-3
- Authors
- Ajiboye, A. R., Abdullah-Arshah, R., Qin, H., & Isah-Kebbe, H.
- Affiliations
- Universiti Malaysia Pahang Al-Sultan Abdullah (MY)
- ORCID
- Qin, H.
- Venue
- International Journal of Computer Systems & Software Engineering
- Published
- 2015-02-15
- Volume
- 1
- Issue
- 1
- Pages
- 75-84
- ISSN
- 2180-0650 (ISSN-L)
- 2289-8522
- OpenAlex
- W996801710
- Cited by
- 104
- References
- 20
- Citations by year
- 2026: 7 · 2025: 13 · 2024: 15 · 2023: 17 · 2022: 23 · 2021: 16 · 2020: 8 · 2019: 3 · 2017: 2
- FWCI
- 0.65
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- International Journal of Computer Systems & Software Engineering (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Universiti Malaysia Pahang
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- dataset size, prediction model, supervised learning, model generalization, training data size, model accuracy, mean absolute error, neural network model
About these checks
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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