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Genomics and Applied Biology
2014, Vol.5, No.3
http://gab.sophiapublisher.com
division of dataset for training and test is shown in
Table 1.
Table 1 The number of healthy and patient data in training and
test sets
Training
Test
Total
Healthy
12
5
17
Patient
23
10
33
Total
35
15
50
The AIS system was trained using training data and its
performance in the test set was recorded. Also, to have
an insight about the result of this study, a widely used
method- Artificial Neural Networks- was also applied
to the same data.
1.2 Used AIS system
The applied method is a simple AIS algorithm that
mimics the clonal selection theory in natural immune
system as many other clonal selection-based AIS
methods (Ada, and Nossal, 1987; Chen, and Mahfouf,
2006; Cutello et al., 2005; L. N. de Castro, and Von
Zuben, 2000; Garrett, 2003; Ong et al., 2005;
Perelson, and Oster, 1979; Timmis, and Neal, 2001).
The biological base for this theory can be found in
(Abbas, 1994). The block diagram of used AIS
method is shown in Figure 1.
Here, the system units are named as Antibodies (
Ab
)
and inputs that are presented to the system are
regarded as Antigens (
Ag
). A random population is
formed in the beginning and the affinity of each
member of this population to a presented input (
Ag
) is
calculated using Euclidean distance criterion given
below in Equation 1:
Affinity=1-D ;where
.
1
1
2
L
k
k
k
Ag
Ab
L
D
Here,
Ab
k
is the k
th
feature of
Ab
vector and
Ag
k
is the
k
th
feature of
Ag
vector.
If the affinity of an
Ab
in the population exceeds a
threshold value named as
supp
, that
Ab
is selected for
cloning. Cloning is done by simply copying the vector
of
Ab
with a number proportional to the
Ab
’s affinity
and after that, a clone population is formed. After
cloning, a mutation procedure is applied to the some
Ab
clones to have diversity in the population. The
mutation is done through changing some values of
Ab
vectors by a new value which is determined randomly.
That is, besides of random determination of which
Ab
s
will be mutated, the selection of features that will be
changed and the determination of new feature values
are also conducted in a random fashion. After these
processes, a number of best
Ab
s in mutated population
is taken for the next iteration (the best
Ab
s are the
Ab
s
whose affinities are highest). Also some randomly
generated
Ab
s are added to these
Ab
s for the use in
next iteration as a beginning population. The iterations
are conducted a number of times a memory population
is formed using best
Ab
s produced after the iterations
conducted for each
Ag
. The class information of that
Ab
is also taken with the same class of presented
Ag
.
As a last step, the memory
Ab
s are deleted from the
memory population, if the affinity of them is higher
that
supp
value with any other memory
Ab
(Figure 1).
Here a memory population including memory
Ab
s and
their class information is formed in training. The
classification procedure on the other hand is done
through finding the nearest memory
Ab
to the
presented
Ag
and class decision about that
Ag
is given
by looking the class information of that nearest
Ab
.
In our application, we conducted 100 iterations for
each presented input data (
Ag
). The 50
Ab
s were used
in the beginning population in each iteration. The
determination of correct
supp
value was done in
experimentally. That is the
supp
value was changed
between 0.9-0.01 and for each experienced
supp
value,
a training-test process was conducted to see the
resulted test classification accuracy in percentage.
Here this accuracy is calculated as:
otherwise
T
i
T
0,
t.c )
classify(t
if
1,
assess(t)
T t ,
)
assess(t
)
accuracy(T
1i
i