Simple learning classifier machine

Abstract EN

A learning classifier system is one of the methods for applying a genetic-based approach to machine learning applications.

An enhanced version of the system that employs the Bucket-brigade algorithm to reward individuals in a chain of co-operating rules is implemented and assigned the task of learning rules for classifying simple objects.

The task is to classify an object that has one or more of the following features: wing, 2-legs/wheels, 3-legs/wheels, 4-legs/wheels, big, flies into one of the following: bird, vehicle.

the main goal is to exploit the ability of the algorithm to perform well in a noisy environment and its ability to make little or no assumption about its problem domain.

Results are presented which show that the system was able to learn rules for the task using only a few training examples and starting with classifiers that were randomly generated.

It is argued that a classifier based learning method requires little training examples and that by its use of genetic algorithms to search for new plausible rules, the method should be able to cope with changing conditions.

Results show also The parallel implementation of the algorithm would speed up the training process.