Computational Intelligence - February 2013 - 74
Table 3 Ensemble network performance without ten fold cross validation.
Classifiers
Hidden neurons
TP [%]
Tn [%]
aCCuraCy [%]
4
5
111, 39, 94, 31
111, 39, 94, 31, 33
47.0
45.0
39.0
43.0
86.0
88.0
6
111, 39, 94, 31, 33
46.0
33.0
79.0
10
111, 39, 94, 31, 33, 62, 87, 97, 149, 8
49.0
37.0
86.0
12
111, 39, 94, 31, 33, 62, 87, 97, 149, 8, 9, 10
45.0
39.0
84.0
13
111, 39, 94, 31, 33, 62, 87, 97, 149, 8, 9,
10, 45
111, 39, 94, 31, 33, 62, 87, 97, 149, 8, 9,
10, 45, 148, 7, 23, 130, 134
47.0
45.0
92.0
46.0
46.0
92.0
18
22
111, 39, 94, 31, 33, 62, 87, 97, 149, 8, 9,
10, 45, 148, 7, 23, 130, 134, 4, 25, 29, 118
48.0
33.0
81.0
25
111, 39, 94, 31, 33, 62, 87, 97, 149, 8, 9, 10,
45, 148, 7, 23, 130, 134, 4, 25, 29, 118, 16,
72, 137
111, 39, 94, 31, 33, 62, 87, 97, 149, 8, 9, 10,
45, 148, 7, 23, 130, 134, 4, 25, 29, 118, 16,
72, 137, 17, 61
49.0
32.0
81.0
47.0
44.0
91.0
27
30
111, 39, 94, 31, 33, 62, 87, 97, 149, 8, 9, 10,
45, 148, 7, 23, 130, 134, 4, 25, 29, 118, 16,
72, 137, 17, 61, 12, 63, 146
48.0
38.0
86.0
35
111, 39, 94, 31, 33, 62, 87, 97, 149, 8, 9,
10, 45, 148, 7, 23, 130, 134, 4, 25, 29, 118,
16, 72, 137, 17, 61, 12, 63, 146, 20, 21, 54,
67, 86
47.0
39.0
86.0
36
111, 39, 94, 31, 33, 62, 87, 97, 149, 8, 9,
10, 45, 148, 7, 23, 130, 134, 4, 25, 29, 118,
16, 72, 137, 17, 61, 12, 63, 146, 20, 21, 54,
67, 86, 6
47.0
43.0
90.0
39
111,39,94,31,33,62,87,97,149,8,9,10,45,148,
7,23,130,134,4,25,29,118,16,72,137,17,61,12,
63,146,20,21,54,67,86,6,36,24,27
47.0
43.0
90.0
as well [37]. A comparison of the techniques employed in this paper together
with the results for breast mass classification obtained by other researchers is
presented in Table 1.
Table 4 Accuracy for Adaboostm1
MLP classifier.
Hidden
neurons
TP [%]
Tn [%]
aCCuraCy
[%]
8
93.0
87.0
90.0
11
86.0
87.0
86.5
16
87.0
86.0
86.5
20
25
87.0
87.0
93.0
87.0
87.0
90.0
40
88.0
83.0
85.5
51
87.0
87.0
87.0
62
88
90.0
85.0
89.0
86.0
87.5
87.5
98
91.0
85.0
88.0
101
92.0
86.0
89.0
107
111
150
74
91.0
91.0
88.0
86.0
87.0
87.0
Fig. 7 shows the performance of the
proposed technique in comparison to
recently published techniques. It is difficult to compare results from one technique to another due to variances in
configuration parameters, research
methodology, datasets as well as other
metrics including time and computing
resources required to reach a classification. Fig. 7 however does contain
research using several techniques that
have been applied to exactly the same
dataset (ensemble MLP, MLP, Adaboost
M1 ensemble, clustered ensemble [35]
together with the proposed technique)
in order to ensure that an appropriate
performance evaluation can be undertaken of different classification techniques. The performance evaluation of
all techniques that has been tested on
the DDSM [21] has been included in
Fig. 7 in order to provide as fair a comparison as possible.
Examination of Fig. 7 indicates that
the performance of the proposed technique is comparable and better than that
achieved by a number of existing techniques. A direct comparison with our
earlier work on a clustered ensemble [35]
indicates that the proposed technique has
performed better on the mass dataset.
While performing the experiments
it was noted that some experiments
took much longer than others to complete. The Adaboost M1 experiments
ran for considerably longer than any
other technique.
As can be seen in Fig. 7, the proposed technique has improved the
classification accuracy, however to see
if the improvement was significant, an
ANOVA analysis was conducted. A 5%
confidence level was used. The
ANOVA summary and analysis are
presented in Tables 5 and 6.
As shown in Table 6, the p-value is significantly below the required 5% confidence level that confirms that the
improvement in performance of the proposed ensemble technique is statistically
significant. Table 5 (ANOVA summary)
shows that the variance for the ensemble
technique is much higher than that of the
MLP. This is because of the following reasons. The classifiers for the MLP were
chosen on the basis of being the highest
performers from the candidate classifier
pool. The classifiers for the ensemble
technique came from a much smaller candidate pool and the variation was therefore
higher as all candidates were chosen.
Table 5 ANOVA summary.
GrouPs
CounT
sum
averaGe
varianCe
MLP
38
3195.5
84.09
0.6197
ProPosed enseMbLe
38
3674.0
96.68
1.3706
89.0
enseMbLe MLP
38
3266.0
85.95
12.4296
87.5
AdAboost MLP
38
3337.5
87.83
0.9497
88.5
IEEE ComputatIonal IntEllIgEnCE magazInE | FEbruary 2013
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