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

Functional Classification of Joints

Functional Classification of Joints

Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...

Structural Classification of Joints

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

Types of Selection

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...

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).

Optimal Foraging

Optimal Foraging

How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.

You might also read

Related Articles

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

Sort by

Same author

Effect of Medicaid coverage of tobacco-dependence treatments on smoking cessation.

International journal of environmental research and public health·2010

Same author

Cytokine and autoantibody patterns in acute liver failure.

Journal of immunotoxicology·2009

Same author

A novel scoring system for prognostic prediction in d-galactosamine/lipopolysaccharide-induced fulminant hepatic failure BALB/c mice.

BMC gastroenterology·2009

Same author

Mammalian target of rapamycin signaling pathway contributes to glioma progression and patients' prognosis.

The Journal of surgical research·2009

Same author

Estrogen receptor neurobiology and its potential for translation into broad spectrum therapeutics for CNS disorders.

Current molecular pharmacology·2009

Same author

Transcriptional and post-translational regulation of adiponectin.

The Biochemical journal·2009

Search research articles

Related Experiment Video

Updated: Nov 19, 2025

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

Feature selection based on fuzzy joint mutual information maximization.

Omar A M Salem^1,2, Feng Liu¹, Ahmed Sobhy Sherif²

¹School of Computer Science, Wuhan University, Wuhan 430072, China.

Mathematical Biosciences and Engineering : MBE

|February 2, 2021

Summary

This summary is machine-generated.

This study introduces Fuzzy Joint Mutual Information Maximization (FJMIM), a novel feature selection method designed to enhance classification accuracy with high-dimensional data. FJMIM effectively addresses limitations in existing techniques, improving system efficiency and stability.

Keywords:

classification systems feature selection fuzzy mutual information fuzzy sets mutual information

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Nov 19, 2025

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

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

Machine Learning
Data Science
Artificial Intelligence

Background:

High-dimensional datasets pose significant challenges for classification systems due to irrelevant or redundant features.
Existing information-theory-based feature selection (FS) methods have limitations in handling continuous features, estimating redundancy, and incorporating outer-class information.

Purpose of the Study:

To introduce a new feature selection method, Fuzzy Joint Mutual Information Maximization (FJMIM), designed to overcome the limitations of current approaches.
To improve classification performance and efficiency in the presence of high-dimensional data.

Main Methods:

Development of the Fuzzy Joint Mutual Information Maximization (FJMIM) algorithm.
Experimental comparison of FJMIM against nine conventional and state-of-the-art feature selection methods.
Validation using 13 benchmark datasets.

Main Results:

FJMIM demonstrates promising improvements in classification performance compared to existing methods.
The proposed method shows enhanced feature selection stability across various datasets.
Experimental results confirm the effectiveness of FJMIM in handling high-dimensional data challenges.

Conclusions:

FJMIM offers a robust solution for feature selection in high-dimensional classification tasks.
The method effectively addresses limitations related to continuous features, redundancy estimation, and outer-class information.
FJMIM represents a significant advancement in improving classification accuracy and feature selection stability.