Performing Classification with an Environment
Manipulating Mutable Automata
Proceedings of the 2000 Congress on Evolutionary
Computation CEC00, стр. 264--271. La Jolla Marriott Hotel La Jolla, California, USA, IEEE Press, (6-9 July 2000)Аннотация
In this paper a novel approach to performing
classification is presented. Hypersurface Discriminant
functions are evolved using Genetic Programming. These
discriminant functions reside in the states of a Finite
State Automata, which has the ability to reason 1 and
logically combine the hypersurfaces to generate a
complex decision space. An object may be classified by
one or many of the discriminant functions, this is
decided by the automata. During the evolution of this
symbiotic architecture, feature selection for each of
the discriminant functions is achieved implicitly, a
task which is normally performed before a
classification algorithm is trained. Since each
dis-criminant function has different features, and
objects may be classified with one or more discriminant
functions, no two objects from the same class need be
classified using the same features. Instead, the most
appropriate features for a given object are used.