REGAL-TC: a distributed genetic algorithm for concept learning based on REGAL and the treatment of counterexamples

Abstract

This paper presents a proposal to improve REGAL, a concept learning system based on a distributed genetic algorithm that learns first-order logic multi-modal concept descriptions in the field of classification tasks. This algorithm has been a pioneer system and source of inspiration for others. Studying the philosophy and experimental behaviour of REGAL, we propose some improvements based principally on a new treatment of counterexamples that promote its underlying goodness in order to achieve better performances in accuracy, interpretability and scalability, so that the new system meets the main requirements for classification rules extraction in data mining. The experimental study carried out shows valuable improvements compared with both REGAL and G-Net distributed genetic algorithms and interesting results compared with some state-of-the-art representative algorithms in this field.

A survey of methods for distributed machine learning

“…On the one hand, there are models based on distributing data artificially between different computational systems, and normally, they move data between them during the execution of the distributed algorithm. In this context, we can find classical techniques such as [10,11] and nowadays improvements like [12,13]. This context is connected to a new computing paradigm namely "big data" processing [14][15][16].…”