An Agent-Based Algorithm for Data Reduction

The paper proposes an agent-based algorithm for data reduction, implemented using JABAT (JADE Based A-Team) environment designed for solving a variety of computationally hard optimization problems. The approach aims at reducing the original dataset in two dimensions including selection of reference instances and removal of irrelevant attributes. Several agents representing different local-search based strategies are employed in parallel to achieve a synergetic effect. The paper includes also computational experiment results.