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Comparing classification algorithms in data mining
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Comparing classification algorithms in data mining
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1774.pdf
Description
Identifier
Thesis
2228
Author
Adu-Poku, Sampson, 1961-
Title
Comparing
classification
algorithms
in
data
mining
Publisher
Central Connecticut State University
Date
2012
Resource Type
Master's Thesis
Notes
In this
thesis
,
support
vector
machines
,
neural
networks
,
logistic
regression
,
naïve
Bayes
,
classification
and
regression
trees
, the
C5.0
algorithms
,
QUEST
,
CHAID
and
discriminant
analysis
have been
implemented
on
nine
real-world
datasets
. The
goal
of the
research
was to
evaluate
the
performance
of the
classification
algorithms
on
both
multiclass
problems
and
binary
classification
problems
using
a
variety
of
performance
metrics
:
classification
accuracy
,
precision
,
recall
(sensitivity)
,
specificity
and
lift
charts/gain
charts
.
We
found
that the
C5.0
algorithm
proved
to have the
best
performance
in
many
of the
datasets
.
Neural
networks
,
naïve
Bayes
,
logistic
regression
also
performed
well
.
However
, there
is
no
universally
best
learning
algorithm
.
None
of the
algorithms
outperformed
the
others
in
every
problem
.
We
found
that
which
algorithm
performed
best
depended
on the
type
of
problem
being
considered
,
dataset
characteristics
and the
performance
matrix
used
.
Subject
Data mining
Department
Department of Mathematical Sciences
Advisor
Larose, Daniel T.
Type
Text
Digital Format
application/pdf
Language
eng
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