基于二元蚁群优化算法的分类规则挖掘  

Classification Rule Mining Based on Binary Ant Colony Optimization Algorithm

作　　者：熊伟清[1] 

机构地区：[1]宁波大学计算机科学与技术研究所,宁波315211

出　　处：《模式识别与人工智能》2008年第4期500-505,共6页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学基金(No.60472099);浙江省自然科学基金(No.Y106080)资助项目

摘　　要：提出一种基于二元蚁群算法的分类规则挖掘算法.针对蚁群算法计算时间长的缺点,引入一种变异算子,同时为了避免蚁群算法陷入局部最优,又引入灾变算子.通过对美国加州大学机器学习数据集中的测试集进行测试表明,该算法的预测准确率能较大提高.实验同时显示引入变异算子和灾变算子能有效节省计算时间和防止陷入局部最优.In this paper, a new algorithm for classification rule mining is proposed, which is based on binary ant colony optimization algorithm. Aiming at the long computing time, a mutation operator is involved. To avoid the local optima problem, a disaster operator is also introduced. The algorithm is applied to the dataset from UCI machine learning repository, and the result shows that the forecasting accuracy is improved greatly. Moreover, by the mutation operator and disaster operator, the computing time can be effectively saved and the local optima can be avoided.

关 键 词：模拟进化算法 二元蚁群优化算法 数据挖掘 分类规则挖掘 机器学习 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]