A Fast Genetic Algorithm for Mining Classification Rules in Large Datasets

Abstract

Nowadays data repositories are huge and are extremely large. Building a rule-based classification model for these huge data sets using a Genetic Algorithm becomes an extremely complex process. This is because during the learning process several passes are made over the training data set by the Genetic Algorithm and this makes it extensively I/O intensive and unsuitable. One way to solve this problem is to build the model incrementally. This paper proposes an incremental Genetic Algorithm that builds the rule-based classification model in a fine granular manner by independently evolving tiny components based on the evolution of the data set which reduces the learning cost and thus makes it scalable to large data sets.