A simple heuristic for classification with ant-miner using a population

作者：Wu, Hongxing^*; Sun, Kai

来源：2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2012, China,Jiangxi,Jian,Jian, 2012-08-26 to 2012-08-27.

DOI：10.1109/IHMSC.2012.67

• Ant Colony Optimization algorithms
• Ant miners
• Classification tasks
• Heuristic
• Heuristic strategy
• Local optimal solution
• Population
• Predictive accuracy

Ant-Miner is an Ant Colony Optimization algorithm for classification task. This paper proposes an improved version of Ant-Miner, named mAnt-Miner , which is based on mAnt-Miner (Ant-Miner that uses a population of many ants). mAnt-Miner uses a simple and invariable heuristic strategy, that avoids it easily trapping in the local optimal solution and improves the efficiency of the algorithm. mAnt-Miner has been compared against Ant-Miner and mAnt-Miner in six public domain data sets. The results show that: 1) in term of predictive accuracy, mAnt-Miner is competitive with Ant-Miner and better than mAnt-Miner;2) mAnt-Miner is faster than Ant-Miner and mAnt-Miner;3) the difference of the rule simplicity between three algorithms is small.

• 出版日期2012
• 单位合肥工业大学