关键词: 异常检测分类, 不均衡数据, 流形学习, 代价敏感学习

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 manifold learning to achieve the dimension reduction, the new algorithm combines AdaCost technology of integrated learning, to improve classification performance. The new algorithm is based on the simulation experiment on the research objection of polymetallic 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.

Key words: anomaly detection, unbalanced data, manifold learning, cost-sensitive learning