Ant Colony Optimization with Three Stages for Independent Test Cost Attribute Reduction

• Published in: Mathematical problems in engineering Vol. 2013; no. 2013; pp. 1 - 11
• Main Authors: Zhu, William, Min, Fan, Zhao, Hong, Xu, Zilong
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2013; John Wiley & Sons, Inc
• Subjects: Ant colony optimization; Artificial intelligence; Construction costs; Cost engineering; Costs; Datasets; Deletion; Filtration; Genetic algorithms; Graphical representations; Information science; International conferences; Machine learning; Mathematical problems; Pattern recognition; Performance evaluation; Rough set models; Set theory
• ISSN: 1024-123X; 1026-7077; 1563-5147; 1563-5147
• DOI: 10.1155/2013/510167

Minimal test cost attribute reduction is an important problem in cost-sensitive learning. Recently, heuristic algorithms including the information gain-based algorithm and the genetic algorithm have been designed for this problem. However, in many cases these algorithms cannot find the optimal solution. In this paper, we develop an ant colony optimization algorithm to tackle this problem. The attribute set is represented as a graph with each vertex corresponding to an attribute and weight of each edge to pheromone. Our algorithm contains three stages, namely, the addition stage, the deletion stage, and the filtration stage. In the addition stage, each ant starts from the initial position and traverses edges probabilistically until the stopping criterion is satisfied. The pheromone of the traveled path is also updated in this process. In the deletion stage, each ant deletes redundant attributes. Two strategies, called the centralized deletion strategy and the distributed deletion strategy, are proposed. Finally, the ant with minimal test cost is selected to construct the reduct in the filtration stage. Experimental results on UCI datasets indicate that the algorithm is significantly better than the information gain-based one. It also outperforms the genetic algorithm on medium-sized dataset Mushroom.