Evolutionary algorithms for fast parallel classification

Abstract

The classification tries to assign the best category to given unknown records based on previous observations. It is clear that with the growing amount of data, any classification algorithm can be very slow. The learning speed of many developed state-of-the-art algorithms like deep neural networks or support vector machines is very low. Evolutionary-based approaches in classification have the same problem.This paper describes five different evolutionary-based approaches that solve the classification problem and run in real time. This was achieved by using GPU parallelization. These classifiers are evaluated on two collections that contains millions of records. The proposed parallel approach is much faster and preserve the same precision as a serial version.