Which Outlier Detection Algorithm Should I Use
作者： Charu C. Aggarwal , Saket Sathe
DOI: 10.1007/978-3-319-54765-7_6
关键词:
摘要: Ensembles can be used to improve the performance of base detectors in several different ways. The first method is use a single detector conjunction with like feature bagging and subsampling. second combine multiple order induce greater diversity. What impact using generic ensemble methods on various detectors? combining these into higher-level combination? This chapter will discuss both ways also which one squeeze most out methods.
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sci-hub.st 参考文章(64)
H.S. Javitz, A. Valdes, The SRI IDES statistical anomaly detector ieee symposium on security and privacy. pp. 316- 326 ,(1991) , 10.1109/RISP.1991.130799
Fabio T Ramos, Lionel Ott, Sanjay Chawla, Linsey Pang, On Integrated Clustering and Outlier Detection neural information processing systems. ,vol. 27, pp. 1359- 1367 ,(2014)
Andrew F. Emmott, Shubhomoy Das, Thomas Dietterich, Alan Fern, Weng-Keen Wong, Systematic construction of anomaly detection benchmarks from real data knowledge discovery and data mining. pp. 16- 21 ,(2013) , 10.1145/2500853.2500858
Leo Belzile, Multivariate Extreme Value Distributions ,(2015)
Guilherme O. Campos, Arthur Zimek, Jörg Sander, Ricardo J. G. B. Campello, Barbora Micenková, Erich Schubert, Ira Assent, Michael E. Houle, On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study Data Mining and Knowledge Discovery. ,vol. 30, pp. 891- 927 ,(2016) , 10.1007/S10618-015-0444-8
Fei Tony Liu, Kai Ming Ting, Zhi-Hua Zhou, Isolation Forest international conference on data mining. pp. 413- 422 ,(2008) , 10.1109/ICDM.2008.17
Markus Goldstein, Seiichi Uchida, A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data PLOS ONE. ,vol. 11, pp. e0152173- ,(2016) , 10.1371/JOURNAL.PONE.0152173
Kai Ming Ting, Ye Zhu, Mark Carman, Yue Zhu, Zhi-Hua Zhou, Overcoming Key Weaknesses of Distance-based Neighbourhood Methods using a Data Dependent Dissimilarity Measure knowledge discovery and data mining. pp. 1205- 1214 ,(2016) , 10.1145/2939672.2939779
G Roy, G Roy, G Roy, O Schrijvers, None, Robust random cut forest based anomaly detection on streams international conference on machine learning. pp. 2712- 2721 ,(2016)
Saket Sathe, Charu Aggarwal, LODES: Local Density Meets Spectral Outlier Detection Proceedings of the 2016 SIAM International Conference on Data Mining. pp. 171- 179 ,(2016) , 10.1137/1.9781611974348.20