MulO-AntMiner: a new ant colony algorithm for the multi-objective classification problem

Authors:
Nesrine Said;Moez Hammami;Khaled Ghedira
Affiliations:
University of Tunis, Higher Institute of Management of Tunis, Computer Science Department, Search Laboratory SOIE;University of Tunis, Higher Institute of Management of Tunis, Computer Science Department, Search Laboratory SOIE;University of Tunis, Higher Institute of Management of Tunis, Computer Science Department, Search Laboratory SOIE
Venue:
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part II
Year:
2011

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Abstract

This paper presents a new ant-based algorithm for the multi-objective classification problem. The new algorithm called MulO-AntMiner (Multi-Objective Ant-Miner) is an improved version of the Ant-Miner algorithm, the first implementation of the ant colony algorithm for discovering classification rules. The fundamental principles in the proposed algorithm are almost similar to those in original Ant-Miner; even though, in our work there are two or more class attributes to be predicted. As a result, the consequent of a classification rule contains multiple predictions, each prediction involving a different class attribute. We have compared the performance of MulO-AntMiner with two other algorithms namely the C4.5 algorithm and the original Ant-Miner.