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 Experiment Videos

Unsupervised feature evaluation: a neuro-fuzzy approach.

S K Pal1, R K De, J Basak

  • 1Machine Intelligence Unit, Indian Statistical Institute, Calcutta, 700035, India. sankar@isical.ac.in

IEEE Transactions on Neural Networks
|February 6, 2008
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Association Areas of the Cortex01:21

Association Areas of the Cortex

Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...

You might also read

Related Articles

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

Sort by
Same author

Outcomes of immune-checkpoint inhibitor rechallenge in metastatic clear-cell renal cell carcinoma: results from a global real-world evidence study.

ESMO open·2026
Same author

Outcomes of immune checkpoint inhibitor rechallenge in advanced urothelial carcinoma: results from a global real-world evidence study.

ESMO open·2025
Same author

Circulating kidney injury molecule-1 (KIM-1) and association with outcome to adjuvant immunotherapy in renal cell carcinoma.

Annals of oncology : official journal of the European Society for Medical Oncology·2025
Same author

The role of microenvironmental pH regulation on colorectal cancer cells - a preliminary study.

Journal of physiology and pharmacology : an official journal of the Polish Physiological Society·2025
Same author

Multicenter analysis of high-dose chemotherapy regimens for the treatment of patients with refractory or recurrent germ cell tumors.

ESMO open·2025
Same author

First-in-human phase I study to evaluate safety, tolerability, pharmacokinetics, pharmacodynamics, immunogenicity, and antitumor activity of PF-07209960 in patients with advanced or metastatic solid tumors.

ESMO open·2025

This study introduces novel neuro-fuzzy methods for unsupervised feature selection and extraction. These approaches identify optimal features and reduce dimensionality without prior cluster knowledge.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Unsupervised learning requires effective feature selection and extraction for pattern recognition.
  • Existing methods often need prior knowledge of cluster numbers or lack flexibility.

Purpose of the Study:

  • To develop novel neuro-fuzzy networks for unsupervised feature selection and extraction.
  • To introduce a flexible fuzzy feature evaluation index and membership function.
  • To perform feature selection and extraction without needing the number of clusters.

Main Methods:

  • Formulation of neuro-fuzzy approaches for feature selection and extraction.
  • Definition of a fuzzy feature evaluation index based on pattern similarity.
  • Introduction of a flexible membership function incorporating weighted distance.

Related Experiment Videos

  • Design of two new layered networks for unsupervised learning.

    • Main Results:

    • A network for feature selection provides an optimal order of individual feature importance.
    • A network for feature extraction identifies optimum transformed features and their relative importance.
    • Dimensionality reduction from n-dimensional to n'-dimensional space (n' < n).
    • Experimental validation demonstrates superiority over related methods.

    Conclusions:

    • The proposed neuro-fuzzy networks effectively perform feature selection and extraction in unsupervised learning.
    • These methods are robust and do not require prior information on the number of clusters.
    • The approach offers a flexible and powerful tool for data analysis and dimensionality reduction.