Is your dataset big enough? Sample size requirements when using artificial neural networks for discrete choice analysis
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Deep learning models require more training data and are not expected to perform well in the first few iterations.
“However, deep learning models require more training data [2] and are not expected to perform well in the first few iterations [39].”
From The SAFE procedure: a practical stopping heuristic for active learning-based screening in systematic reviews and meta-analyses (2024) · cites this paper as [2]
Section 4 (page unavailable) reports better predictive performance when ANNs are trained on larger datasets. But the Conclusions specify that the study’s findings come from shallow ANNs, not deep learning models, so the claim extends beyond the models studied. The separate claim about performance in the first few iterations is attached to citation [39], not this source. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe performance of neural networks in classification problems increases with dataset sample size.
“as Alwosheel et al. show, the performance of neural networks in classification problems increases with dataset sample size (Alwosheel et al., 2018).”
From Active learning-based systematic reviewing using switching classification models: the case of the onset, maintenance, and relapse of depressive disorders (2023) · cites this paper as Alwosheel et al., 2018
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“However, deep learning models require more training data [2] and are not expected to perform well in the first few iterations [39].”
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- Authors
- Alwosheel, A., van Cranenburgh, S., & Chorus, C. G.
- Affiliations
- Delft University of Technology (NL)
- Corresponding author
- Alwosheel, Ahmad
- ORCID
- Chorus, Caspar G.
- Venue
- Journal of Choice Modelling
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 28
- Pages
- 167-182
- ISSN
- 1755-5345 (ISSN-L)
- OpenAlex
- W2884443195
- Cited by
- 524
- References
- 48
- Citations by year
- 2026: 71 · 2025: 127 · 2024: 113 · 2023: 82 · 2022: 59 · 2021: 43 · 2020: 19 · 2019: 9 · 2018: 1
- FWCI
- 27.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Choice Modelling (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Funders
- King Abdulaziz City for Science and Technology
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economic and Environmental Valuation
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Keywords
- artificial neural networks, discrete choice analysis, sample size requirements, rules of thumb, model complexity, random utility maximization, random regret minimization, Monte Carlo simulation, synthetic data
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