模式识别与人工智能
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模式识别与人工智能  2012, Vol. 25 Issue (4): 617-623    DOI:
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基于变精度粗糙集的KNN分类改进算法
余鹰1,2,苗夺谦1,刘财辉1,王磊1
1。同济大学计算机科学与技术系上海201804
2。江西农业大学软件学院南昌330045
An Improved KNN Algorithm Based on Variable Precision Rough Sets
YU Ying1,2, MIAO Duo-Qian1, LIU Cai-Hui1, WANG Lei1
1.Department of Computer Science and Technology,Tongji University,Shanghai 201804
2.School of Software,Jiangxi Agricultural University,Nanchang 330045

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摘要 传统KNN算法具有简单、稳定和高效的特点,在实际领域得到广泛应用。但算法的时间复杂度与样本规模成正比,大规模或高维数据会降低KNN分类效率。文中通过引入变精度粗糙集模型,提出一种改进的KNN分类算法。算法运用变精度粗糙集上下近似概念,将各类训练样本划分为核心和边界区域,分类过程计算新样本与各类的近似程度,获取新样本的归属区域,减小分类代价,增强算法的鲁棒性。实验表明,与传统KNN算法相比,文中算法保持较高的分类精度并有效提高分类效率,具有一定的理论与实际价值。
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关键词 K最近邻(KNN)变精度粗糙集上下近似    
AbstractK Nearest Neighbor (KNN) is a simple, stable and effective supervised classification algorithm in machine learning and is used in many practical applications. Its complexity increases with the number of instances, and thus it is not practicable for large-scale or high dimensional data. In this paper, an improved KNN algorithm based on variable parameter rough set model (RSKNN) is proposed. By introducing the concept of upper and lower approximations in variable precision rough set model, the instances of each class are classified into core and boundary areas, and the distribution of the training set is obtained. For a new instance, RSKNN firstly computes the area it belongs to. Then, according to the area information, the algorithm determines the category directly or searches k-nearest neighbors among the related areas instead of all areas. In this way, the computing cost is reduced and the robustness is enhanced. The experimental results for selected UCI datasets show that the proposed method is more effective than the traditional KNN with high classification accuracy.
Key wordsK Nearest Neighbor (KNN)    Variable Precision Rough Set    Upper and Lower Approximation   
收稿日期: 2011-06-24     
ZTFLH: TP391  
  TP181  
基金资助:国家自然科学基金资助项目(No.60970061,61075056,61103067)
作者简介: 余鹰,女,1979年生,博士研究生,主要研究方向为粗糙集理论、数据挖掘。E-mail:yuyingjx@163。com。苗夺谦,男,1964年生,教授,博士生导师,主要研究方向为粗糙集理论、智能信息处理等。刘财辉,男,1979年生,博士研究生,主要研究方向为粗糙集、机器学习。王磊,男,1976年生,博士研究生,主要研究方向为智能信息处理、数据挖掘。
引用本文:   
余鹰,苗夺谦,刘财辉,王磊. 基于变精度粗糙集的KNN分类改进算法[J]. 模式识别与人工智能, 2012, 25(4): 617-623. YU Ying, MIAO Duo-Qian, LIU Cai-Hui, WANG Lei. An Improved KNN Algorithm Based on Variable Precision Rough Sets. , 2012, 25(4): 617-623.
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