Handling continuous attributes in Ant Colony Classification algorithms

Abstract

Most real-world classification problems involve continuous (real-valued) attributes, as well as, nominal (discrete) attributes. The majority of ant colony optimisation (ACO) classification algorithms have the limitation of only being able to cope with nominal attributes directly. Extending the approach for coping with continuous attributes presented by cAnt-Miner (Ant-Miner coping with continuous attributes), in this paper we propose two new methods for handling continuous attributes in ACO classification algorithms. The first method allows a more flexible representation of continuous attributes' intervals. The second method explores the problem of attribute interaction, which originates from the way that continuous attributes are handled in cAnt-Miner, in order to implement an improved pheromone updating method. Empirical evaluation on eight publicly available data sets shows that the proposed methods facilitate the discovery of more accurate classification models.