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CCSU Theses & Dissertations
Applying Misclassification Costs to Ameliorate the False Positive Rate in Bioassay Screening
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Applying Misclassification Costs to Ameliorate the False Positive Rate in Bioassay Screening
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Description
Identifier
Thesis
2323
Author
Batta, Kay Sharon, 1946-
Title
Applying
Misclassification
Costs
to
Ameliorate
the
False
Positive
Rate
in
Bioassay
Screening
Publisher
Central Connecticut State University
Date of Publication
2013
Resource Type
Master's Thesis
Abstract
This
thesis
evaluates
and
extends
work
done
in the
Journal
of
Cheminformatics
article
"
Virtual
screening
of
bioassay
data
" by
Amanda
Schierz
(2009)
and the
datasets
it
provides
.
Schierz
was
able
to
locate
several
primary
datasets
in
PubChem
, a
repository
of
bioassay
data
, and their
corresponding
secondary
screening
results
.
Bioassay
data
is
very
imbalanced
in the
sense
that
only
a
few
substances
will
show
an
effect
on the
target
protein
and,
hence
,
can
possibly
be
developed
into
medicines
.
Schierz
experimented
with
several
data
mining
tools
in the
software
package
WEKA
using
misclassification
costs
to
improve
results
and
found
that the
optimum
misclassification
cost
was
dependent
on
which
data
mining
tool
was
used
. The
purpose
of this
study
is
to
see
if there are
other
factors
that also
affect
the
optimum
misclassification
cost
. The
C5.0
data
mining
tool
in
IBM
SPSS
Modeler
was
used
and
is
similar
to the
algorithm
called
MetaCost
J48
in
WEKA
.
Using
Modeler
,
C5.0
,
gave
much
better
results
than
MetaCost
J48
and in
three
to
six
minutes
instead
of the
60
minutes
taken
by the
WEKA
algorithm
. The
optimum
misclassification
cost
was
very
different
. For
Modeler
it
occurred
at
cost
469
with a
true
positive
rate
of
92%
and a
false
positive
rate
of
4.76%
. For
MetaCost
J48
it
occurred
at
cost
1500
with a
true
positive
rate
of
54%
. The
false
positive
rate
was not
given
for
J48
but the
best
WEKA
algorithm
(CSC
SMO)
had a
true
positive
rate
of
76.92%
and a
false
positive
rate
of
11.91%
.
Experiments
within
C5.0
showed
that how the
parameters
were
set
affected
the
results
. For
example
,
changing
the
pruning
severity
from the
default
of
75%
to
25%
changed
the
optimum
misclassification
cost
from
42
with a
true
positive
rate
of
85%
and a
false
positive
rate
of
1.11%
to an
optimum
misclassification
cost
of
469
with a
true
positive
rate
of
92%
and a
false
positive
rate
of
4.76%
.
Changing
the
number
of
predictors
also
affected
the
optimum
misclassification
cost
.
Notes
"
Submitted
in
Partial
Fulfillment
of the
Requirements
for the
Degree
of
Master
of
Science
in
Data
Mining.
";
Thesis
advisor
:
Daniel
Larose.
;
M.S.,Central
Connecticut
State
University,,2013.
;
Includes
bibliographical
references
(leaves
69-71)
.
Subject
Biological assay.
Classification.
Errors, Scientific--Costs.
Department
Department of Mathematical Sciences
Advisor
Larose, Daniel T.
Type
Text
Digital Format
application/pdf
Software
System requirements: PC and World Wide Web browser.
Language
eng
OCLC number
856520444
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