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Related Concept Videos

Random Sampling Method01:09

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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...
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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. 
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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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Sampling materials are classified into three main types: solid, liquid, and gas.
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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.
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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RACOG and wRACOG: Two Probabilistic Oversampling Techniques.

Barnan Das1, Narayanan C Krishnan1, Diane J Cook1

  • 1School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164.

IEEE Transactions on Knowledge and Data Engineering
|April 5, 2016
PubMed
Summary

New Gibbs sampling methods, RACOG and wRACOG, improve machine learning for imbalanced datasets by generating synthetic minority class samples. These techniques enhance model performance by better representing under-represented data classes.

Keywords:
Gibbs samplingImbalanced class distributionMarkov chain Monte Carlo (MCMC)oversampling

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Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Class imbalance is a common challenge in machine learning, where minority classes are under-represented.
  • Existing oversampling methods often fail to adequately represent the minority class distribution, leading to misclassification errors.

Purpose of the Study:

  • To introduce novel Gibbs sampling-based oversampling approaches, RACOG and wRACOG, for addressing class imbalance.
  • To improve the synthetic generation and strategic selection of minority class samples.

Main Methods:

  • Developed RACOG and wRACOG, which utilize Gibbs sampling to generate new minority class samples based on joint attribute probability distributions.
  • RACOG selects samples using a predefined lag, while wRACOG prioritizes samples with a high probability of misclassification.

Main Results:

  • Validated RACOG and wRACOG on UCI datasets modified for class imbalance and a dataset with extreme imbalance.
  • Compared the performance of the proposed methods against three existing resampling techniques.

Conclusions:

  • The proposed Gibbs sampling-based oversampling methods offer a promising approach to mitigate class imbalance in machine learning.
  • RACOG and wRACOG demonstrate potential for improving classification accuracy in imbalanced datasets.