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Stratified Sampling Method

Stratified Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...

Cluster Sampling Method

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Related Experiment Video

Updated: Jun 18, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Data splitting for artificial neural networks using SOM-based stratified sampling.

R J May¹, H R Maier, G C Dandy

¹Research and Development, United Water, Adelaide, SA 5001, Australia. robert.may@uwi.com.au

Neural Networks : the Official Journal of the International Neural Network Society

|December 5, 2009

Summary

This summary is machine-generated.

Choosing the right data splitting method is crucial for artificial neural network (ANN) generalization. A novel self-organizing map (SOM) based stratified sampling approach offers reliable, high-quality data subsets for ANN development, especially for large datasets.

Related Experiment Videos

Last Updated: Jun 18, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

Artificial Intelligence
Machine Learning
Data Science

Background:

Data splitting is critical for artificial neural network (ANN) generalization using hold-out cross-validation.
The sampling methodology significantly impacts training, testing, and validation subset quality, affecting model performance variability.
Current practices often overlook the choice of sampling methodology, despite its importance in ANN development.

Purpose of the Study:

To investigate the variability in subset quality across different data splitting approaches for ANNs.
To introduce and evaluate a novel stratified sampling method based on self-organizing maps (SOMs).
To provide guidelines for optimizing SOM size and sample allocation to minimize bias and variance.

Main Methods:

Development of a novel stratified sampling approach using Neyman sampling of self-organizing maps (SOMs).
Evaluation of the SOM-based approach against random sampling, DUPLEX, systematic stratified sampling, and trial-and-error sampling.
Application to an ANN function approximation task to assess subset quality and model performance.

Main Results:

The DUPLEX method demonstrated benchmark performance with consistent model results and no variability.
The SOM-based approach reliably generated high-quality samples, comparable to DUPLEX.
The SOM-based method showed particular effectiveness for non-uniform datasets and scalability for large datasets.

Conclusions:

The SOM-based stratified sampling approach provides a confident and reliable method for data splitting in ANN development.
This novel method is especially beneficial for non-uniform datasets and large-scale applications.
Further consideration of sampling methodology is essential for improving ANN model accuracy and reliability.