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3.2 Discussion
From the tables above, we can see that the
improvements brought to CLONALG show
effective. The choice of the initial antibodies to
launch an AIS algorithm directly affects the results,
it is necessary that these antibodies represent all
learning classes and not just some examples only, it
will allow to find the cell which represents most
exactly the example to learn. The creation of local
subgroups from learning classes has treated this
problem in each of the both proposed approaches.
The creation of the cloning cell also played an
important role in the learning phase, in Cells Clonal
Selection AIS, the creation of this cell was done by
calculating an average cell of the best memory cells,
while in median Filter AIS, the median cell was
created from the median values of each attribute of
the matrix of the nearest memory cells, and to
maximize the affinity of each cell created, they are
compared to the best antibodies and they are added
to the set of memory cells only if they are better. No
cell can be representative in next generation was
rejected.
Classification results after 20 iterations on
DDSM taking into account three new descriptors
proposed in (Cheikhrouhou, 2012) are 98.71% for
cells clonal selection AIS and 98.21% for Median
Filter for AIS, efficient results compared to other
approaches AIS.
The classification results of each one of the new
descriptors separately prove that the proposed
approaches prove the effectiveness of the proposed
techniques comparing to the SVM classifier.
4 CONCLUSION
In this work, the classification of DDSM by Clonal
Selection-Based AIS was presented. Each of the two
techniques deals with the problem of initialization of
the antibodies before the launching of the training
Phase for the representation of the entirety of the
data to learn and proposes a new method for the
choice of the cell to clone in order to maximize
affinity with the training example. Cells Clonal
Selection AIS proposes the creation of an average
cell from the closest memory cells, and Median
Filter AIS introduces the principle of the median
filter to create a median cell for the cloning. The
obtained results indicate that the improvements
brought to CLONALG are effective and can be of a
precious help to the experts for a second opinion in
their diagnosis of breast cancer.
Note that we have made is that the two
approaches in particular and AIS algorithms
generally require heavy computation time , our next
work will focus on the treatment of this problem.
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