developed algorithm has shown classification
accuracy that not concedes to other generalization
algorithms, and in some cases surpasses them.
Average accuracy of classification is approximately
88.9%. It is necessary to note that the classification
accuracy received by our algorithm is much above
that the classification accuracy achieved by methods
of an induction of deciding trees (ID3, ID4, ID5R,
C4.5) at the solving the majority of the problems. It
is explained by the impossibility of representation of
the description of some target concepts as a tree.
Moreover, it is possible to note that combining of
search of significant attributes and the discretization
procedure is very useful. Most clearly, it is visible
from the results received at the decision of the
Australian credit task. It is possible to explain by the
presence in these data the attributes both with
continuous and with discrete domains. The
modification of the search procedure of significant
attributes is directed namely to processing of such
combination.
6 CONCLUSIONS
The method of reasoning by analogy on the basis of
structural analogy was considered from the aspect of
its application in modern IDSS, in particular, for a
solution of problems of real-time diagnostics and
forecasting. The example of the algorithm for
solution search on the basis of analogy of properties
that takes into account the context was proposed.
This algorithm uses a modified structure of analogy
that is capable of taking into account not one
property (as in the base algorithm), but a set of
properties. These properties determine the original
context of analogy and transfer from the source to
the receiver only those facts that are relevant in the
context of the constructed analogy.
The presented method was applied at
implementation of a prototype of IDSS on the basis
of non-classical logics for monitoring and control of
complex objects like power units.
We have also considered the concept
generalization problem and the approach to its
decision based on the rough set theory. The heuristic
discretization algorithm directed towards the
decreasing of time and memory consumption has
been proposed. It is based on Jonson’s strategy and
extension of idea of iterative calculation number of
pairs of objects discerned by a cut. Also the search
algorithm of the significant attributes combined with
the stage of discretization is developed. It allows to
avoid splitting into intervals of continuous domains
of insignificant attributes.
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