摘要
数据挖掘领域中类别不平衡数据分类属于热门研究课题。在传统分类算法中,由于存在一定程度的偏向性,使得少数类的分类效果欠佳。基于此,提出一种多层级联式少数类聚类高精度数据挖掘算法。该算法基于聚类进行欠采样,在多数类样本上进行聚类并提取聚类质心,得到数目等同少数类样本的聚类质心,之后和所有少数类样例一起构建新平衡训练集。为杜绝少数类样本数量过少导致训练集过小而影响分类精度,利用SMOTE过采样结合聚类欠采样,在平衡训练集上通过K均值聚类和C4.5决策树算法相级联的分类方式来优化分类决策的边界。实验表明,该算法在处理类别不平衡数据分类问题方面具备一定的优势。
In the field of machine learning and data excavating, the classification of imbalanced data is a hot research topic. In the traditional classification algorithm, the existence of a certain degree of bias makes the classification of a small number poor. To solve this problem, a new algorithm for clustering high precision data excavating with multi-class cascade is proposed. Based on clustering, the algorithm constructs a new balanced training set, which is based on SMOTE. And K means clustering is used to cluster the training samples into K clusters, and the C4.5 algorithm mean clustering algorithm is used to optimize the classification decision by means of K clustering. The algorithm is based on C4.5 algorithm. Experiments show that this algorithm has certain advantages in dealing with the problem of data classification.
作者
许统德
赵志俊
高俊文
XU Tong-de;ZHAO Zhi-jun;GAO Jun-wen(Teaching Affairs Office, Guangdong Agriculture Industry Business Polytechnic, Guangzhou 510507, China;Sontan College, Guangzhou University, Guangzhou 511370, China)
出处
《控制工程》
CSCD
北大核心
2018年第5期829-834,共6页
Control Engineering of China
基金
广东省高等教育研究立项课题(201401154)
关键词
数据挖掘
少数类分类
多层级
K均值聚类
C4.5决策树
Data excavating
classification
multi class
multi-level
K means clustering
C4.5 decision tree
