摘要
提出了一种K-最近邻改进算法,该算法用模糊自适应共振理论(Fuzzy ART)对K-最近邻的训练样本集进行浓缩,以改善K-最近邻的计算速度.该算法首先用Fuzzy ART将训练样本集中的每一类样本进行聚类,减小了训练样本集的数据量,提高了算法的计算速度,保持了预测精度,从而使该算法适用于海量数据集的情况.实验表明,该算法适用于对复杂而数据量较大的数据库进行分类.
This paper implemented an improved K-Nearest Neighbor (Fuzzy KNN) algorithm in which Fuzzy Adaptive Resonance Theory (ART) is applied in K-NN classification to make a new algorithm. Fuzzy ART clustering is carried out to select the subset of the training set, which can reduce the volume of the training set and lead to computational efficiency. Experiments on two UCI databases showed it had more efficiency on the lager databases.
出处
《河北工业大学学报》
CAS
2004年第6期1-5,共5页
Journal of Hebei University of Technology
基金
国家自然科学基金资助项目(60275020)河北省教委基金资助项目(401023)
关键词
模糊自适应共振理论
K-最近邻分类
聚类
分类
Fuzzy Adaptive Resonance Theory
K- Nearest Neighbor
clustering
classification
