Impact of Dataset Characteristics on Optimal Model Selection: A Comparative Analysis of Simulated and Real-World Data

Harald H. Rietdijk; Olayemi Shola Alabi; Patricia Conde-Cespedes; Talko B. Dijkhuis; Hilbrand K.E. Oldenhuis; Maria Trocan

doi:10.1109/ISCAS56072.2025.11044173

Impact of Dataset Characteristics on Optimal Model Selection: A Comparative Analysis of Simulated and Real-World Data

Harald H. Rietdijk
, Olayemi Shola Alabi
, Patricia Conde-Cespedes
, Talko B. Dijkhuis
, Hilbrand K.E. Oldenhuis
, Maria Trocan

Isep

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

Abstract

In the rapidly evolving field of Machine Learning , selecting the most appropriate model for a given dataset is crucial. Understanding the characteristics of a dataset can significantly influence the outcomes of predictive modeling efforts, making the study of the properties of the dataset an essential component of data science. This study investigates the possibilities of using simulated human data for personalized applications, specifically for testing clustering approaches. In particular, the study focuses on the relationship between dataset characteristics and the selection of the optimal classification model for clusters of datasets. The results of this study provide critical insights for researchers and practitioners in machine learning, emphasizing the importance of dataset characteristics and variability in building and selecting robust models for diverse data conditions. The use of human simulation data provide valuable insights but requires further refinement to capture the full variability of real-world conditions.

Translated title of the contribution	Impact van datasetkenmerken op optimale modelkeuze: een vergelijkende analyse van gesimuleerde en echte gegevens
Original language	English
Title of host publication	Proceedings - IEEE International Symposium on Circuits and Systems
Publisher	Institute of Electrical and Electronics Engineers
Pages	1-5
Number of pages	5
ISBN (Electronic)	979-8-3503-5683-0
ISBN (Print)	979-8-3503-5684-7
DOIs	https://doi.org/10.1109/ISCAS56072.2025.11044173
Publication status	Published - 25 May 2025

Publication series

Series	Proceedings - IEEE International Symposium on Circuits and Systems

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 03 Good Health and Well-being

Keywords

data characteristics
machine learning
model selection
simulated data

Research Focus Areas Hanze University of Applied Sciences * (mandatory by Hanze)

Healthy Ageing
Entrepreneurship

Research Focus Areas Research Centre or Centre of Expertise * (mandatory by Hanze)

Technology and digitalization

Publinova themes

Technology
ICT and Media
Health

Access to Document

10.1109/ISCAS56072.2025.11044173

ISCAS 2025, IEEE International Symposium on Circuits and Systems
Rietdijk, H. (Speaker)
25 May 2025 → 28 May 2025
Activity: Talk or presentation › Oral presentation

Cite this

Rietdijk, H. H., Shola Alabi, O., Conde-Cespedes, P., Dijkhuis, T. B., Oldenhuis, H. K. E., & Trocan, M. (2025). Impact of Dataset Characteristics on Optimal Model Selection: A Comparative Analysis of Simulated and Real-World Data. In Proceedings - IEEE International Symposium on Circuits and Systems (pp. 1-5). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ISCAS56072.2025.11044173

Rietdijk, Harald H. ; Shola Alabi, Olayemi ; Conde-Cespedes, Patricia et al. / Impact of Dataset Characteristics on Optimal Model Selection : A Comparative Analysis of Simulated and Real-World Data. Proceedings - IEEE International Symposium on Circuits and Systems. Institute of Electrical and Electronics Engineers, 2025. pp. 1-5 (Proceedings - IEEE International Symposium on Circuits and Systems).

@inbook{7df3d923b71e43938b4f7fce69c9c3d1,

title = "Impact of Dataset Characteristics on Optimal Model Selection: A Comparative Analysis of Simulated and Real-World Data",

abstract = "In the rapidly evolving field of Machine Learning , selecting the most appropriate model for a given dataset is crucial. Understanding the characteristics of a dataset can significantly influence the outcomes of predictive modeling efforts, making the study of the properties of the dataset an essential component of data science. This study investigates the possibilities of using simulated human data for personalized applications, specifically for testing clustering approaches. In particular, the study focuses on the relationship between dataset characteristics and the selection of the optimal classification model for clusters of datasets. The results of this study provide critical insights for researchers and practitioners in machine learning, emphasizing the importance of dataset characteristics and variability in building and selecting robust models for diverse data conditions. The use of human simulation data provide valuable insights but requires further refinement to capture the full variability of real-world conditions.",

keywords = "data characteristics, machine learning, model selection, simulated data, machinaal leren, gesimuleerde data, model selectie, gegevenskarakteristieken",

author = "Rietdijk, \{Harald H.\} and \{Shola Alabi\}, Olayemi and Patricia Conde-Cespedes and Dijkhuis, \{Talko B.\} and Oldenhuis, \{Hilbrand K.E.\} and Maria Trocan",

year = "2025",

month = may,

day = "25",

doi = "10.1109/ISCAS56072.2025.11044173",

language = "English",

isbn = "979-8-3503-5684-7",

series = "Proceedings - IEEE International Symposium on Circuits and Systems",

publisher = "Institute of Electrical and Electronics Engineers",

pages = "1--5",

booktitle = "Proceedings - IEEE International Symposium on Circuits and Systems",

address = "United States",

}

Rietdijk, HH, Shola Alabi, O, Conde-Cespedes, P, Dijkhuis, TB , Oldenhuis, HKE & Trocan, M 2025, Impact of Dataset Characteristics on Optimal Model Selection: A Comparative Analysis of Simulated and Real-World Data. in Proceedings - IEEE International Symposium on Circuits and Systems. Institute of Electrical and Electronics Engineers, Proceedings - IEEE International Symposium on Circuits and Systems, pp. 1-5. https://doi.org/10.1109/ISCAS56072.2025.11044173

Impact of Dataset Characteristics on Optimal Model Selection: A Comparative Analysis of Simulated and Real-World Data. / Rietdijk, Harald H.; Shola Alabi, Olayemi; Conde-Cespedes, Patricia et al.
Proceedings - IEEE International Symposium on Circuits and Systems. Institute of Electrical and Electronics Engineers, 2025. p. 1-5 (Proceedings - IEEE International Symposium on Circuits and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - Impact of Dataset Characteristics on Optimal Model Selection

T2 - A Comparative Analysis of Simulated and Real-World Data

AU - Rietdijk, Harald H.

AU - Shola Alabi, Olayemi

AU - Conde-Cespedes, Patricia

AU - Dijkhuis, Talko B.

AU - Oldenhuis, Hilbrand K.E.

AU - Trocan, Maria

PY - 2025/5/25

Y1 - 2025/5/25

N2 - In the rapidly evolving field of Machine Learning , selecting the most appropriate model for a given dataset is crucial. Understanding the characteristics of a dataset can significantly influence the outcomes of predictive modeling efforts, making the study of the properties of the dataset an essential component of data science. This study investigates the possibilities of using simulated human data for personalized applications, specifically for testing clustering approaches. In particular, the study focuses on the relationship between dataset characteristics and the selection of the optimal classification model for clusters of datasets. The results of this study provide critical insights for researchers and practitioners in machine learning, emphasizing the importance of dataset characteristics and variability in building and selecting robust models for diverse data conditions. The use of human simulation data provide valuable insights but requires further refinement to capture the full variability of real-world conditions.

AB - In the rapidly evolving field of Machine Learning , selecting the most appropriate model for a given dataset is crucial. Understanding the characteristics of a dataset can significantly influence the outcomes of predictive modeling efforts, making the study of the properties of the dataset an essential component of data science. This study investigates the possibilities of using simulated human data for personalized applications, specifically for testing clustering approaches. In particular, the study focuses on the relationship between dataset characteristics and the selection of the optimal classification model for clusters of datasets. The results of this study provide critical insights for researchers and practitioners in machine learning, emphasizing the importance of dataset characteristics and variability in building and selecting robust models for diverse data conditions. The use of human simulation data provide valuable insights but requires further refinement to capture the full variability of real-world conditions.

KW - data characteristics

KW - machine learning

KW - model selection

KW - simulated data

KW - machinaal leren

KW - gesimuleerde data

KW - model selectie

KW - gegevenskarakteristieken

U2 - 10.1109/ISCAS56072.2025.11044173

DO - 10.1109/ISCAS56072.2025.11044173

M3 - Chapter

SN - 979-8-3503-5684-7

T3 - Proceedings - IEEE International Symposium on Circuits and Systems

SP - 1

EP - 5

BT - Proceedings - IEEE International Symposium on Circuits and Systems

PB - Institute of Electrical and Electronics Engineers

ER -

Rietdijk HH, Shola Alabi O, Conde-Cespedes P, Dijkhuis TB , Oldenhuis HKE, Trocan M. Impact of Dataset Characteristics on Optimal Model Selection: A Comparative Analysis of Simulated and Real-World Data. In Proceedings - IEEE International Symposium on Circuits and Systems. Institute of Electrical and Electronics Engineers. 2025. p. 1-5. (Proceedings - IEEE International Symposium on Circuits and Systems). doi: 10.1109/ISCAS56072.2025.11044173

Impact of Dataset Characteristics on Optimal Model Selection: A Comparative Analysis of Simulated and Real-World Data

Abstract

Publication series

UN SDGs

Keywords

Research Focus Areas Hanze University of Applied Sciences * (mandatory by Hanze)

Research Focus Areas Research Centre or Centre of Expertise * (mandatory by Hanze)

Publinova themes

Access to Document

Fingerprint

Activities

ISCAS 2025, IEEE International Symposium on Circuits and Systems

Cite this