An agent-based approach to the multiple-objective selection of reference vectors

The paper proposes an agent-based approach to the multiple-objective selection of reference vectors from original datasets. Effective and dependable selection procedures are of vital importance to machine learning and data mining. The suggested approach is based on the multiple agent paradigm. The authors propose using JABAT middleware as a tool and the original instance reduction procedure as a method for selecting reference vectors under multiple objectives. The paper contains a brief introduction to the multiple objective optimization, followed by the formulation of the multiple-objective, agent-based, reference vectors selection optimization problem. Further sections of the paper provide details on the proposed algorithm generating a non-dominated (or Pareto-optimal) set of reference vector sets. To validate the approach the computational experiment has been planned and carried out. Presentation and discussion of experiment results conclude the paper.