Loading [MathJax]/extensions/TeX/ieee_stixext.js
A Fuzzy Integral Approach for Ensembling Unsupervised Feature Selection Algorithms | IEEE Conference Publication | IEEE Xplore

A Fuzzy Integral Approach for Ensembling Unsupervised Feature Selection Algorithms


Abstract:

Feature selection is an effective technique for decreasing data dimensionality by selecting a significant feature set. Gathering label information can be time-consuming a...Show More

Abstract:

Feature selection is an effective technique for decreasing data dimensionality by selecting a significant feature set. Gathering label information can be time-consuming and expensive, as labeled instances are not always available. Therefore, unsupervised learning importance has emerged. In this article, a new unsupervised feature selection is presented based on an ensemble strategy. The ensemble of multiple feature selection methods is performed using fuzzy integral operators. The comparisons are made against various feature selection methods in the literature to show the better performance of the proposed method. These comparisons are conducted based on classification accuracy and run-time.
Date of Conference: 25-26 January 2023
Date Added to IEEE Xplore: 26 April 2023
ISBN Information:
Conference Location: Tehran, Iran, Islamic Republic of

References

References is not available for this document.