Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

Random Variables

Random Variables

A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...

Classification of Signals

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...

Expected Frequencies in Goodness-of-Fit Tests

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

Residuals and Least-Squares Property

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

Regression Toward the Mean

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Sexual abuse or traffic accidental trauma? Analysis of a case.

La Clinica terapeutica·2025

Same author

A regional-based newborn hearing screening program: the Emilia-Romagna model after ten years of legislation.

Annali di igiene : medicina preventiva e di comunita·2022

Same author

Congenital lung malformations: a novel application for lung ultrasound?

Journal of ultrasound·2019

Same author

Late-presenting right congenital diaphragmatic hernia with severe hypotrophy of the right lobe of the liver.

International journal of surgery case reports·2015

Same author

Chloroplast Microsatellite Diversity in Phaseolus vulgaris.

Frontiers in plant science·2013

Same author

Response to Snodgrass and Bush.

Journal of pediatric urology·2012

Search research articles

Related Experiment Video

Updated: Jul 7, 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

Evolved feature weighting for random subspace classifier.

L Nanni¹, A Lumini

¹DEIS, IEIIT- Universita di Bologna, 40136 Bologna, Italy. loris.nanni@unibo.it

IEEE Transactions on Neural Networks

|February 14, 2008

Summary

This study introduces an evolved feature weighting approach for random subspace methods (RSM) to improve multiclassifier generation. Particle swarm optimization (PSO) efficiently finds weights, significantly reducing error rates and enhancing performance on benchmark datasets.

Related Experiment Videos

Last Updated: Jul 7, 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
Data Mining
Artificial Intelligence

Background:

Random subspace method (RSM) constructs classifiers in random feature subspaces.
Optimizing feature weights within subspaces is crucial for classifier performance.
Existing methods may not efficiently determine optimal feature weights.

Purpose of the Study:

To propose an evolved feature weighting approach for RSM.
To minimize the error rate in the training set by assigning weights to features within each subspace.
To leverage particle swarm optimization (PSO) for efficient weight determination.

Main Methods:

Implementation of an evolved feature weighting strategy within each random subspace.
Utilizing particle swarm optimization (PSO) to find optimal weight factors for each feature.
Experimental validation using several benchmark datasets.

Main Results:

The proposed evolved feature weighting approach significantly reduces error rates.
Demonstrated performance improvement compared to state-of-the-art methods.
Effective weight optimization achieved through PSO in random subspaces.

Conclusions:

The evolved feature weighting approach enhances multiclassifier generation via RSM.
PSO provides an efficient mechanism for optimizing feature weights in random subspaces.
The method shows superior performance on diverse benchmark datasets, validating its effectiveness.