Review of feature selection approaches based on grouping of features
Özet
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 works findings
can guide effective design of new FS approaches using feature grouping.
