Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.8K
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...
7.8K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.8K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.8K
Midrange01:07

Midrange

3.8K
A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
3.8K
Reducing Line Loss01:18

Reducing Line Loss

188
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
188
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

621
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
621
Improving Translational Accuracy02:07

Improving Translational Accuracy

11.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
11.8K

You might also read

Related Articles

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

Sort by
Same author

Birdsongs recognition based on ensemble ELM with multi-strategy differential evolution.

Scientific reports·2022
Same author

Applications of machine learning in pine nuts classification.

Scientific reports·2022
Same author

Birdsong classification based on ensemble multi-scale convolutional neural network.

Scientific reports·2022
Same author

Impact of Strain-Induced Changes in Defect Chemistry on Catalytic Activity of Nd<sub>2</sub>NiO<sub>4+δ</sub> Electrodes.

ACS applied materials & interfaces·2018
Same author

Curcumin induces apoptosis and inhibits angiogenesis in murine malignant mesothelioma.

International journal of oncology·2018
Same author

Deletion of SMARCA4 impairs alveolar epithelial type II cells proliferation and aggravates pulmonary fibrosis in mice.

Genes & diseases·2018

Related Experiment Video

Updated: Aug 29, 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

7.6K

A new improved maximal relevance and minimal redundancy method based on feature subset.

Shanshan Xie1, Yan Zhang2, Danjv Lv1

  • 1College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, 650224 China.

The Journal of Supercomputing
|September 5, 2022
PubMed
Summary

This study introduces an improved maximal relevance and minimal redundancy (ImRMR) method for effective feature selection. ImRMR enhances pattern recognition by reducing data dimensions and improving classification performance.

Keywords:
Feature selectionFeature subsetImRMRSequence forward searchmRMR

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

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

Published on: March 1, 2024

859
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

Related Experiment Videos

Last Updated: Aug 29, 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

7.6K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

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

Published on: March 1, 2024

859
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

Area of Science:

  • Computer Science
  • Data Mining
  • Machine Learning

Background:

  • Feature selection is crucial for successful pattern recognition and data mining.
  • Existing methods like maximal relevance and minimal redundancy (mRMR) have limitations.

Purpose of the Study:

  • To propose an improved maximal relevance and minimal redundancy (ImRMR) feature selection method based on feature subsets.
  • To enhance the efficiency and accuracy of feature selection in data mining and pattern recognition.

Main Methods:

  • The ImRMR method utilizes Pearson correlation coefficient and mutual information to measure feature relevance.
  • It introduces a factor to adjust the weights of relevance measurement criteria.
  • Candidate feature subsets are generated using an equal grouping method and ranked via incremental search, with final selection using sequential forward search and classification algorithms.

Main Results:

  • Experiments on seven datasets demonstrate ImRMR's effectiveness in removing irrelevant and redundant features.
  • The method successfully reduces feature dimensionality and model training/prediction time.
  • ImRMR leads to significant improvements in classification performance.

Conclusions:

  • The proposed ImRMR feature selection method is effective for pattern recognition and data mining.
  • It offers a robust approach to dimensionality reduction and performance enhancement.
  • ImRMR provides a valuable tool for optimizing machine learning models.