计算机工程与应用
計算機工程與應用
계산궤공정여응용
COMPUTER ENGINEERING AND APPLICATIONS
2013年
13期
105-109
,共5页
异常检测分类%不均衡数据%流形学习%代价敏感学习
異常檢測分類%不均衡數據%流形學習%代價敏感學習
이상검측분류%불균형수거%류형학습%대개민감학습
anomaly detection%unbalanced data%manifold learning%cost-sensitive learning
化探异常识别是成矿预测的重要依据。化探异常识别本质上是一不均衡数据的分类问题。异常识别过程中面临的主要问题是高维数据的处理问题，流形学习通过非线性降维方法实现维数约简。提出了一种基于流形学习的异常识别算法，通过流形学习进行维数约简，结合AdaCost技术，以改善不平衡数据的分类性能。以某锡铜多金属矿床的数据为研究对象进行仿真实验，实验结果表明该算法能够更准确地圈定区域化探异常，为成矿预测与评价提供了新的解决途径。
化探異常識彆是成礦預測的重要依據。化探異常識彆本質上是一不均衡數據的分類問題。異常識彆過程中麵臨的主要問題是高維數據的處理問題，流形學習通過非線性降維方法實現維數約簡。提齣瞭一種基于流形學習的異常識彆算法，通過流形學習進行維數約簡，結閤AdaCost技術，以改善不平衡數據的分類性能。以某錫銅多金屬礦床的數據為研究對象進行倣真實驗，實驗結果錶明該算法能夠更準確地圈定區域化探異常，為成礦預測與評價提供瞭新的解決途徑。
화탐이상식별시성광예측적중요의거。화탐이상식별본질상시일불균형수거적분류문제。이상식별과정중면림적주요문제시고유수거적처리문제，류형학습통과비선성강유방법실현유수약간。제출료일충기우류형학습적이상식별산법，통과류형학습진행유수약간，결합AdaCost기술，이개선불평형수거적분류성능。이모석동다금속광상적수거위연구대상진행방진실험，실험결과표명해산법능구경준학지권정구역화탐이상，위성광예측여평개제공료신적해결도경。
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.
