一种新的组合k-近邻预测方法  被引量:4

New Combined k-Nearest Neighbor Predictor

在线阅读下载全文

作  者:何亮[1] 宋擒豹[1] 沈钧毅[1] 海振[1] 

机构地区:[1]西安交通大学计算机科学与技术系,西安710049

出  处:《西安交通大学学报》2009年第4期5-9,共5页Journal of Xi'an Jiaotong University

基  金:国家自然科学基金重大研究规划资助项目(90718024);国家高技术研究发展计划资助项目(2006AA01Z183)

摘  要:针对传统是一近邻(k-NN)算法基于单一k值预测难以兼顾不同样本的个性,从而导致总体预测精度不够理想的问题,提出了一种组合Bk-NN预测方法.首先通过Boosting理论建立了个性化预测模型集,然后分别采用每个模型对样本进行独立预测,最后各模型预测值的加权和将作为最终预测结果.Bk-NN预测充分考虑了不同类型的样本可能要求不同的预测模型与之相适应的情况,有效降低了预测误差.与其他方法不同的是,Bk-NN预测对数据集的属性类型没有特殊要求.在标准数据集上的实验结果表明,Bk—NN预测精度比传统k—NN方法平均提高了6.44%~15.25%.The prediction accuracy of various instances can hardly be ensured by the existing k- nearest neighbor (k-NN) predictor, which works under a single k value. A combined k-NN algorithm, Bk-NN predictor, is proposed in this paper. The novel Bk-NN algorithm is used to build up a set of prediction models based on the Boosting principle, and then each model is used to predict a new instance respectively. The final predicted value of this instance is the weighted sum of these prediction values. The prediction error is reduced since the circumstance that various instances demand specific prediction models to match with is thoroughly taken into account by the Bk-NN algorithm. Moreover, the Bk-NN predictor can work well with both discrete and continuous attribute values. The experimental results on standard datasets show that, compared with the traditional k-NN predictor, the prediction accuracies of the Bk-NN predictor are improved by 6.44%-15.25%.

关 键 词:近邻算法 预测模型 Boosting理论 组合方法 

分 类 号:TP274[自动化与计算机技术—检测技术与自动化装置]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象