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Sampling Methods: Overview01:06

Sampling Methods: Overview

548
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
548
Cluster Sampling Method01:20

Cluster Sampling Method

12.9K
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.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
12.9K
Random Sampling Method01:09

Random Sampling Method

12.6K
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. Data are the result of sampling from a 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. Among the various sampling methods used by...
12.6K
Convenience Sampling Method00:55

Convenience Sampling Method

9.7K
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. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
9.7K
Upsampling01:22

Upsampling

330
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
330
Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

480
Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
480

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Related Experiment Video

Updated: Sep 22, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K

Bayesian network-based over-sampling method (BOSME) with application to indirect cost-sensitive learning.

Rosario Delgado1, J David Núñez-González2,3

  • 1Department of Mathematics, Universitat Autònoma de Barcelona, Campus de la UAB, 08193, Cerdanyola del Vallès, Spain. delgado@mat.uab.cat.

Scientific Reports
|May 24, 2022
PubMed
Summary
This summary is machine-generated.

The Bayesian network-based over-sampling method (BOSME) effectively addresses imbalanced data classification. BOSME outperforms SMOTE by generating synthetic minority instances using Bayesian networks, improving minority class prediction.

Related Experiment Videos

Last Updated: Sep 22, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.7K

Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Supervised learning algorithms struggle with imbalanced datasets, often misclassifying minority classes.
  • Existing over-sampling methods generate synthetic data but may not capture underlying class distributions effectively.

Purpose of the Study:

  • To introduce a novel over-sampling technique, Bayesian network-based over-sampling method (BOSME), for imbalanced data classification.
  • To evaluate BOSME's performance against the Synthetic Minority Over-sampling Technique (SMOTE) in cost-sensitive learning scenarios.

Main Methods:

  • Learned Bayesian networks from minority class instances to model their probability distribution.
  • Generated synthetic minority class instances guided by the learned Bayesian network's probability distribution.
  • Compared BOSME with SMOTE using state-of-the-art classifiers and various datasets under indirect cost-sensitive learning.

Main Results:

  • BOSME demonstrated statistically significant improvements over SMOTE in terms of expected misclassification cost.
  • The proposed method showed enhanced ability to predict the minority class in imbalanced datasets.
  • Experiments confirmed BOSME's effectiveness across different classifiers and datasets.

Conclusions:

  • BOSME offers a superior approach to handling imbalanced data compared to traditional methods like SMOTE.
  • The Bayesian network-based instance generation effectively preserves and utilizes the minority class distribution for better classification.
  • This methodology provides a promising direction for improving machine learning model performance on imbalanced datasets.