Review of feature selection approaches based on grouping of
By A Mystery Man Writer
Description
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 review of recent approaches on wrapper feature selection for
Feature selection using hierarchical feature clustering
GediNET for discovering gene associations across diseases using knowledge based machine learning approach. - Abstract - Europe PMC
Frontiers microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach
How to Choose a Feature Selection Method For Machine Learning
GediNET for discovering gene associations across diseases using knowledge based machine learning approach. - Abstract - Europe PMC
Feature selection and prioritization schema. The feature selection
How to Choose a Feature Selection Method For Machine Learning
Venn diagrams showing the intersection of top 50 extracted
PDF) Review of feature selection approaches based on grouping of
from
per adult (price varies by group size)