Facing Big Data by an agent-based multimodal evolutionary approach to classification

Multi-agent systems recently gained a lot of attention for solving machine learning and data mining problems. Furthermore, their peculiar divide-and-conquer approach is appealing when large datasets have to be analyzed. In this paper, we propose a multi-agent classification system able to tackle large datasets where each agent independently explores a random small portion of the overall dataset, searching for meaningful clusters in proper subspaces where they are well-formed (i.e., compact and populated). This search is orchestrated by means of a genetic algorithm able to act in a multi-modal fashion, since meaningful clusters might lie in different subspaces. Furthermore, since agents operate independently one another, their execution is parallelized across different computational units. Tests show that the proposed algorithm, E-ABC2, is able to deal with large datasets, returning satisfactory results in terms of scalability and performances, especially when compared with our previous baseline versions.