Ant Colony Optimization with Three Stages for Independent Test Cost Attribute Reduction
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 solut...
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| Published in | Mathematical problems in engineering Vol. 2013; no. 2013; pp. 1 - 11 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Cairo, Egypt
Hindawi Publishing Corporation
01.01.2013
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1024-123X 1026-7077 1563-5147 1563-5147 |
| DOI | 10.1155/2013/510167 |
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| Abstract | 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. |
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| AbstractList | 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. (ProQuest: ... denotes formulae and/or non-USASCII text omitted; see image) 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. |
| Author | Xu, Zilong Min, Fan Zhu, William Zhao, Hong |
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| Cites_doi | 10.1016/j.ins.2012.07.010 10.1016/j.ins.2008.05.010 10.1155/2013/946070 10.1016/j.patrec.2009.10.013 10.1016/0022-0000(93)90048-2 10.1016/S0020-0255(03)00056-2 10.1007/BF01001956 10.1109/TKDE.2007.1044 10.1016/j.ins.2006.06.009 10.1109/69.806935 10.1016/j.artint.2010.04.018 10.1016/j.ins.2008.05.024 10.1016/j.patrec.2008.02.006 10.1007/11811305_69 10.1016/j.ins.2011.07.010 10.1016/j.ins.2009.03.007 10.1613/jair.120 |
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| Copyright | Copyright © 2013 Zilong Xu et al. Copyright © 2013 Zilong Xu et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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| References_xml | – volume: 152 start-page: 217 year: 2003 end-page: 230 ident: 16 article-title: Reduction and axiomization of covering generalized rough sets – reference: Deng D. Wang J. Li X. Parallel reducts in a series of decision subsystems Proceedings of the International Joint Conference on Computational Sciences and Optimization (CSO '09) April 2009 Hainan, China 377 380 2-s2.0-70449428895 10.1109/CSO.2009.250 – reference: Deng D. Parallel reduct and its properties Proceedings of the International Conference on Granular Computing (GRC '09) August 2009 Nanchang, China 121 125 2-s2.0-70450060227 10.1109/GRC.2009.5255146 – reference: Min F. Bai Z. He M. Liu Q. The reduct problem with specified attributes Proceedings of the Rough Sets and Soft Computing in Intelligent Agent and Web Technology, International Workshop at WI-IAT 2005 36 42 – reference: Liu Q. Chen L. Zhang J. Min F. 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| SubjectTerms | 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 |
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| Title | Ant Colony Optimization with Three Stages for Independent Test Cost Attribute Reduction |
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