IEEE Transactions on Evolutionary Computation,
Journal Year:
2022,
Volume and Issue:
27(3), P. 595 - 609
Published: May 16, 2022
With
the
increase
of
number
features
and
sample
size,
existing
feature
selection
(FS)
methods
based
on
evolutionary
optimization
still
face
challenges
such
as
"curse
dimensionality"
high
computational
cost.
In
view
this,
dividing
or
clustering
spaces
at
same
time,
this
article
proposes
a
hybrid
FS
algorithm
using
surrogate
sample-assisted
particle
swarm
(SS-PSO).
First,
nonrepetitive
uniform
sampling
strategy
is
employed
to
divide
whole
set
into
several
small-size
subsets.
Regarding
each
subset
unit,
next,
collaborative
mechanism
proposed
space,
with
purpose
reducing
both
cost
search
space
PSO.
Following
that,
an
ensemble
surrogate-assisted
integer
PSO
proposed.
To
ensure
prediction
accuracy
when
evaluating
particles,
construction
management
designed.
Since
replaced
by
small
units,
SS-PSO
significantly
reduces
particles
in
Finally,
applied
some
typical
datasets,
compared
six
algorithms,
well
its
variant
algorithms.
The
experimental
results
show
that
can
obtain
good
subsets
smallest
most
datasets.
All
verify
highly
competitive
method
for
high-dimensional
FS.
