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Genomics and Applied Biology
2014, Vol.5, No.3
http://gab.sophiapublisher.com
2 Results
As stated in the previous section, the first application
study was conducted with AIS and the optimum value
of
supp
value was searched through changing its value
in 0.01-0.9 interval to have maximum test
classification accuracy. Normally, used data and
system units are normalized vectors and the affinity
value lie only in the [0-1] interval. So,
supp
value
should be selected in that interval and we begun with
0.9 value and decrease it with 0.1. Then, around some
values which give high classification accuracies we
decreased or increased
supp
value more tenderly. That
is, we first had a rough insight about
supp
values and
scrutinized some good values in a more detailed way.
The test classification accuracies for searched
supp
values are given in Figure 2.
Figure 2 The change of test classification accuracy with regard
to the change in
supp
value
As shown from the figure, the maximum test
classification accuracy was detected as 93.33% for
0.07, 0.18 and 0.25 values for the
supp
value. Because
15 data in the test procedure is used, this accuracy
means that only one data is misclassified by the
system. Figure 2 shows that there isn’t a specific way
of determining
supp
value. That is, we cannot say that
the accuracy decreases as
supp
increases or otherwise.
The only way of finding best
supp
value is to search
for
supp
value through experimentation.
The other application of prostate cancer classification
was with ANN method. As stated in Section 2,
gradient descent learning algorithm was taken for a
one-hidden layered ANN. The searched parameters in
this application are optimum number of hidden nodes
(
hn
), learning rate (
lr
) and momentum constant (
mc
).
In this search procedure, firstly by fixing
lr
and
mc
to
values of 2 and 0.8,
hn
is changed between 1 and 50
by steps of 1and for each experimented
hn
values, the
test classification accuracy was recorded. The change
of test classification accuracy, according to the
hn
values is shown in Figure 3.
As can be seen from the figure, 100% test
classification accuracy was reached for some hidden
node numbers like 16, 35, 40 and 45. Thus we took
hn
as 16. Searches for best
lr
and
mc
parameters were
also done but, because the 100% was reached, these
are not presented here.
Figure 3 Change of test classification accuracy according to the
changing
hn
number
In summary, ANN has reached a higher result than
AIS but two points should be emphasized here:
The difference was only for one test data.
That is ANN has correctly classified one
more data
The number of dataset is not satisfactory to
have a confidential comparison between two
systems.
Anymore, both methods have well performed
for this classification task and encouraged us
to use these systems with much more data in
real applications of daily life.
3 Discussion
Application of some artificial intelligence methods to
the classification of some disease has increasing day
by day. While this kind of systems cannot be used in
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
50
55
60
65
70
75
80
85
90
95
X: 0.25
Y: 93.33
supp value
test classificationaccuracy (%)
0 5 10 15 20 25 30 35 40 45 50
30
40
50
60
70
80
90
100
hn number
test classificationaccuracy (%)