Unsupervised feature selection: A fuzzy multi-criteria decision-making approach

Article Type:
Research/Original Article (دارای رتبه معتبر)
Feature selection (FS) has shown remarkable performance in decreasing the dimensionality of high-dimensional datasets by selecting a good subset of features. Labeling high-dimensional data can be expensive and time-consuming as labeled samples are not always available. Therefore, providing effective unsupervised FS methods is essential in machine learning. This article provides a fuzzy multi-criteria decision-making method for unsupervised FS in which an ensemble of unsupervised FS rankers is utilized to evaluate the features. These methods are aggregated based on a fuzzy TOPSIS method. This is the first time a fuzzy multi-criteria decision-making approach has been used for an FS problem. Multiple comparisons are made to show the optimality and effectiveness of the proposed strategy against multiple competing FS methods. Our approach regarding two classification metrics, F-score and accuracy, appears superior to comparable  strategies. Also, it is performing so swiftly.
Iranian journal of fuzzy systems, Volume:20 Issue: 7, Nov-Dec 2023
55 to 70
دانلود و مطالعه متن این مقاله با یکی از روشهای زیر امکان پذیر است:
اشتراک شخصی
با عضویت و پرداخت آنلاین حق اشتراک یک‌ساله به مبلغ 1,390,000ريال می‌توانید 70 عنوان مطلب دانلود کنید!
اشتراک سازمانی
به کتابخانه دانشگاه یا محل کار خود پیشنهاد کنید تا اشتراک سازمانی این پایگاه را برای دسترسی نامحدود همه کاربران به متن مطالب تهیه نمایند!
  • حق عضویت دریافتی صرف حمایت از نشریات عضو و نگهداری، تکمیل و توسعه مگیران می‌شود.
  • پرداخت حق اشتراک و دانلود مقالات اجازه بازنشر آن در سایر رسانه‌های چاپی و دیجیتال را به کاربر نمی‌دهد.
In order to view content subscription is required

Personal subscription
Subscribe magiran.com for 70 € euros via PayPal and download 70 articles during a year.
Organization subscription
Please contact us to subscribe your university or library for unlimited access!