JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (5): 195-202.doi: 10.6040/j.issn.1672-3961.0.2017.180

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Weighted hyper-ellipsoidal support vector data description with negative samples for outlier detection

YAO Yu, FENG Jian*, ZHANG Huaguang, HAN Kezhen   

  1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China
  • Received:2017-02-10 Online:2017-10-20 Published:2017-02-10

Abstract: To solve the influence of the imbalance between positive and negative samples in training sample set, a method named weighted hyper-ellipsoidal support vector data description with negative samples(WNESVDD)was proposed. Mahalanobis distance was introduced such that the information of sample distribution was completely considered. Both normal and negative samples were utilized to modeling. Cost-sensitive learning was introduced to set different weights for different classes. The results showed that the empty areas that decision boundary enclosed were reduced effectively and the decision boundary was refined in the proposed method. The data utilization rate was obviously improved. Several experiments on University of California at Irvine(UCI)data sets and the data set from the semi-conductor manufacturing process were conducted. The experiments results showed that the proposed method had strong ability of anomaly detection, and compared with the similar method, false positives and false negatives were dramatically reduced.

Key words: Mahalanobis distance, geometric center of boundary, empty area, outlier detection, sample imbalance, hyper-ellipsoidal support vector support vector data description

CLC Number: 

  • TP181
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