Review of feature selection approaches based on grouping of
4.9 (495) In stock
With the rapid development in technology, large amounts of high-dimensional data have been generated. This high dimensionality including redundancy and irrelevancy poses a great challenge in data analysis and decision making. Feature selection (FS) is an effective way to reduce dimensionality by eliminating redundant and irrelevant data. Most traditional FS approaches score and rank each feature individually; and then perform FS either by eliminating lower ranked features or by retaining highly-ranked features. In this review, we discuss an emerging approach to FS that is based on initially grouping features, then scoring groups of features rather than scoring individual features. Despite the presence of reviews on clustering and FS algorithms, to the best of our knowledge, this is the first review focusing on FS techniques based on grouping. The typical idea behind FS through grouping is to generate groups of similar features with dissimilarity between groups, then select representative features from each cluster. Approaches under supervised, unsupervised, semi supervised and integrative frameworks are explored. The comparison of experimental results indicates the effectiveness of sequential, optimization-based (i.e., fuzzy or evolutionary), hybrid and multi-method approaches. When it comes to biological data, the involvement of external biological sources can improve analysis results. We hope this work’s findings can guide effective design of new FS approaches using feature grouping.
A systematic review of emerging feature selection optimization
Comparison of the number of selected features on twelve datasets
miRGediNET: A comprehensive examination of common genes in miRNA-Target interactions and disease associations: Insights from a grouping-scoring-modeling approach - ScienceDirect
Exploring Wrapper Methods for Optimal Feature Selection in Machine
State of the art of the clustering-based feature selection approaches.
Design of feature selection algorithm for high-dimensional network data based on supervised discriminant projection [PeerJ]
PDF) An Improved Fuzzy Feature Clustering and Selection based on Chi-Squared-Test
GediNET for discovering gene associations across diseases using knowledge based machine learning approach. - Abstract - Europe PMC
A grouping feature selection method based on feature interaction
A grouping feature selection method based on feature interaction
A novel incomplete hesitant fuzzy information supplement and clustering method for large-scale group decision-making [PeerJ]
Review of feature selection approaches based on grouping of
New feature selection methods based on opposition-based learning
Frontiers A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction
Frontiers A Review of Feature Selection Methods for Machine
131 Weis Markets Reviews weismarkets.com @ PissedConsumer
Sustainability, Free Full-Text
Commercial Cannabis Have Your Say Monterey
Melissa Cidade - Survey Statistician - U.S. Census Bureau
Megan Weis, DrPH, MPH, MCHES - Director of Connecting Communities
- Portable WiFi IP Mini Camera Night vision Remote View P2P Wireless Micro Webcam Wearable Video Recorder
- An ultra sexy end to the year
- Greenco Small Plastic Cups for Kids, 5 pcs, Toddler Cups, Kid Glass, Drinking Glasses, Outdoor Cups, Reusable, Unbreakable, Colorful
- This Swim Pad Will Make Your Summer More Epic Than You Ever
- Megacore-máquina para hacer Pilates, equipo de aluminio para deportes comerciales, multifitness, gimnasio, Yoga, plegable - AliExpress
- S Abdominal Binder Post Surgery for Men and Women