计算机工程与应用Issue(13)：105-109,5.DOI:10.3778/j.issn.1002-8331.1111-0210
基于流形学习的异常检测算法研究
Manifold learning-based anomaly detection algorithm
摘要
Abstract
Anomaly detection has important significance in many fields. Essentially speaking, the recognition of geochemical anomalies is the problem of imbalanced data classification. The main problems faced by anomaly identification is the processing problems of high-dimensional data, manifold learning is a nonlinear dimensionality reduction method that can reasonably reduce the data dimension. Therefore this paper proposes an anomaly detection algorithm based on the manifold learning, through mani-fold learning to achieve the dimension reduction, the new algorithm combines AdaCost technology of integrated learning, to im-prove classification performance. The new algorithm is based on the simulation experiment on the research objection of polyme-tallic deposits such as tin and copper from Gejiu, Yunnan province. The experimental results show that predicted results for the new algorithm delineating regional geochemical anomalies are better than traditional methods, which can more accurately identify the forming-ore abnormality.关键词
异常检测分类/不均衡数据/流形学习/代价敏感学习Key words
anomaly detection/unbalanced data/manifold learning/cost-sensitive learning分类
信息技术与安全科学引用本文复制引用
刘凯伟,张冬梅..基于流形学习的异常检测算法研究[J].计算机工程与应用,2013,(13):105-109,5.基金项目
国家自然科学基金（No.40972206）；中央高校基本科研业务费专项资金资助项目（No.1323520909）。 （）
