本文已被：浏览 1331次 下载 2561次
Received:August 16, 2014 Revised:October 08, 2014
Received:August 16, 2014 Revised:October 08, 2014
中文摘要: 动态分类器集成选择(DCES)是当前集成学习领域中一个非常重要的研究方向. 然而, 当前大部分DCES算法的计算复杂度较高. 为了解决该问题和进一步提高算法的性能, 本文提出了基于聚类的动态分类器集成选择(CDCES), 该方法通过对测试样本聚类, 极大地减少了动态选择分类器的次数, 因而降低了算法的计算复杂度. 同时, CDCES是一种更加通用的算法, 传统的静态选择性集成和动态分类器集成为本算法的特殊情况, 因而本算法是一种鲁棒性更强的算法. 通过对UCI数据集进行测试, 以及与其他算法作比较, 说明本算法是一种有效的、计算复杂度较低的方法.
Abstract:Dynamic classifier ensemble selection (DCES) is an important field in the machine learning. However, computational complexity of the current methods is very high. In order to solve the problem and improve the performance further, cluster based dynamic classifier ensemble selection (CDCES) is proposed in this paper. Using the proposed method to cluster the testing sample, the degree of DCES is reduced enormously and the computation complexity is decreased. At the same time, CDCES is a more general method and the traditional static ensemble selection and dynamic classifier is a specific case of the proposed method, so CDCES is more robust. Compared with the other algorithms on UCI data set, it is demonstrated that the proposed method is a more effective and lower computational complexity method.
文章编号： 中图分类号： 文献标志码：
基金项目:国家自然科学基金(61004069)
| Author Name | Affiliation |
| WANG Ning-Yan | Xi'an Communication Institute, Xi'an 710106, China |
| HAN Xiao-Xia | 502 Unit, Second Artillery Institute, Xi'an 710025, China |
| Author Name | Affiliation |
| WANG Ning-Yan | Xi'an Communication Institute, Xi'an 710106, China |
| HAN Xiao-Xia | 502 Unit, Second Artillery Institute, Xi'an 710025, China |
引用文本：
王宁燕,韩晓霞.聚类的动态分类器集成选择.计算机系统应用,2015,24(4):205-208
WANG Ning-Yan,HAN Xiao-Xia.Cluster-Based Dynamic Classifier Ensemble Selection.COMPUTER SYSTEMS APPLICATIONS,2015,24(4):205-208
王宁燕,韩晓霞.聚类的动态分类器集成选择.计算机系统应用,2015,24(4):205-208
WANG Ning-Yan,HAN Xiao-Xia.Cluster-Based Dynamic Classifier Ensemble Selection.COMPUTER SYSTEMS APPLICATIONS,2015,24(4):205-208
