The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
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 [74,78-80].”
From Automated Defect Detection and Decision-Support in Gas Turbine Blade Inspection (2021) · cites this paper as [79]
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Estimating Dataset Size Requirements for Classifying DNA Microarray Data
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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 331
Data-driven personal thermal comfort prediction: A literature review
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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 86
Bhuiyan, M. A. E., Yang, F., Biswas, N. K., Rahat, S. H., & Neelam, T. J. (2020) · Forecasting · doi:10.3390/forecast2030014 · Cited by 84
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
Metadata
- 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
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