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Fig. 2.
Performance analysis of the bootstrap module
Next, the effects of parameters are studied for two different measures: (i)
recognition accuracy and (ii) size of the immune memory. Results are reported here
for the new training algorithm. Almost similar effects have been observed on both the
datasets and results on DS1 are shown in Fig 3. Finally, the effect of k in k-nearest
neighbour classification is examined and it is observed that k = 5 gives the best
performance. Recognition accuracies for different values of k are shown in Fig. 4.
The overall results reported in Table 1 are obtained with k = 5, stimulation threshold
= 0.89, number of resources = 400, mutation rate = 0.008, affinity threshold scalar,
α
= 0.4, hyper-mutation rate = 2 and clonal rate = 10 (the last two parameters are used
in Algorithm-I of section 2).
Classification results are further grouped into three classes,
correct
: a sample is
properly classified;
incorrect
: a sample is wrongly classified, and
reject
: the system
cannot classify a sample. A rejection is reported when no single class gets majority
among the k choices returned by the classifier. Table 3 presents the average classifica-
tion results taking these three aspects into consideration.
