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Weighted Mean

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...

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...

Upsampling

Upsampling

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...

Bootstrapping

Bootstrapping

The term "bootstrap" originated in the 19th century as a metaphor for self-improvement or achieving something independently, without external assistance. This concept extends to statistical bootstrapping, a self-contained method for estimating population parameters through resampling, even though it can be computationally intensive. Developed by the American statistician Dr. Bradley Efron in 1979, bootstrapping provides a robust way to perform inference when the original sample size is small or...

Systematic Sampling Method

Systematic 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. 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.
Systematic sampling is one of the simplest methods...

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 13, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

An approach for classification of highly imbalanced data using weighting and undersampling.

Ashish Anand¹, Ganesan Pugalenthi, Gary B Fogel

¹School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore.

|April 23, 2010

Summary

This study introduces a novel undersampling technique to address data imbalance in machine learning. The method enhances classifier sensitivity on imbalanced biological datasets compared to existing approaches.

Related Experiment Videos

Last Updated: Jun 13, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Machine Learning
Bioinformatics
Data Science

Background:

Real-world datasets frequently exhibit class imbalance, posing challenges for machine learning algorithms.
Standard techniques like support-vector machines may underperform on imbalanced data, impacting diagnostic accuracy.

Purpose of the Study:

To propose and evaluate a new undersampling technique for handling imbalanced datasets.
To improve classifier performance, specifically sensitivity, in the context of biological data.

Main Methods:

Developed a novel undersampling method focused on majority class instance selection.
Evaluated the technique on multiple real-world biological datasets with varying imbalance ratios (9:1 to 100:1).

Main Results:

The proposed undersampling technique significantly improved classifier sensitivity.
Performance gains were observed in comparison to weighted support-vector machines and existing literature results for the same datasets.

Conclusions:

The novel undersampling method effectively addresses data imbalance in biological datasets.
This approach offers a promising strategy for enhancing machine learning model sensitivity in imbalanced classification tasks.