An associative classifier based on the concept of analogy and human learning

Associationism is one of the dominant theories of learning. It is believed to play an implicit role in formation of inert cognition when provided with proper reinforcements. On the other hand learning by analogy plays an important role in the learning process of humans. Instructors all over the world use analogies to put their point through. We have combined these theories in an artificial life environment to construct an associative classifier that is capable of classifying complex classification datasets using an evolving network that adapts itself to the patterns observed. The results thus achieved are promising.