1 |
Z. Zhi-Hua, Ensemble Learning, National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China, 2012.
|
2 |
T. K. Ho, J. J. Hull, and S. N. Srihari, Decision combination in multiple classifier systems, IEEE Trans. Pattern Analysis Machine Intell. 16 (1994), no. 1, 66-75.
DOI
|
3 |
R. Shebuti and A. Leman, An ensemble approach for event detection in dynamic graphs, in KDD ODD2 Workshop (New York, USA) 2014.
|
4 |
D. Khullar, A. K. Jha, and A. B. Jena, Reducing diagnostic errors-why now, New England. J. Med 373 (2015), 2491-2493.
DOI
|
5 |
N. Isadora et al., Ensemble learning method for outlier detection and its application to astronomical light curves, The Astronomical J. 152 (2016), no. 3, 71:1-13.
|
6 |
D. Hawkins, Identification of Outliers, Chapman and Hall, London, 1980.
|
7 |
M. Milou, Outlier detection in datasets with mixed-attributes, Vrije Universiteit Amsterdam (Sept. 2015), Thesis [online: https://beta. vu.nl/nl/Images/stageverslag-meltzer_tcm235-614959.pdf, last accessed March 31, 2019].
|
8 |
E. Schubert, A. Zimek, and H. P. Kriegel, Local outlier detection reconsidered: A generalized view on locality with applications to spatial, video, and network outlier detection, Data Mining Knowledge Discovery 28 (2014), no. 1, 190-237.
DOI
|
9 |
B. Van Stein, M. Van Leeuwen, and T. Back, Local subspace-based outlier detection using global neighborhoods (Gloss), in Proc. IEEE Int. Conf. Big Data (Washington, DC, USA) Dec. 2016, pp. 1136-1142.
|
10 |
H.-P. Kriegel et al., LoOP: Local outlier probabilities, in Proc. ACM Conf. Inf. Knowledge Mananag. (Hong Kong, China) Nov. 2009, pp. 1649-1652.
|
11 |
M. Breuning et al., LOF: Identifying density based local outliers, in Proc. ACM SIGMOD Int. Conf. Manag. Data (Dallas, TX, USA), 2000, pp. 93-104.
|
12 |
G. Giacinto and F. Roli, A theoretical framework for dynamic classifier selection, in Proc. Int. conf. Pattern, Recogn. (Barcelona, Spain), Sept. 2000, pp. 8-11.
|
13 |
A. S. Britto, R. Sabourin, and L. E. S. Oliveira, Dynamic selection of classifiers - a comprehensive review, Pattern Recogn. 47 (2014), no. 11, 3665-3680.
DOI
|
14 |
R. Polikar, Ensemble based systems in decision making, IEEE Circuits Syst. Mag. 6 (2006), no. 3, 21-45.
DOI
|
15 |
K. Woods, W. P. Kegelmeyer, and K. Bowyer, Combination of multiple classifiers using local accuracy estimates, IEEE Trans. Pattern Analysis Machine Intell. 19 (1997), no. 4, 405-410.
DOI
|
16 |
A. H. R. Ko, R. Sabourin, and A. S. Britto, From dynamic classifier selection to dynamic ensemble selection, Pattern Recogn. 41 (2008), no. 5, 1735-1748.
|
17 |
R. M. O. Cruz, R. Sabourin, and G. D. Cavalcanti, Dynamic classifier selection: recent advances and perspectives, Inf. Fusion 41 (2018), 195-216.
DOI
|
18 |
A. Lazarevic and V. Kumar, Feature bagging for outlier detection, in Proc. ACM SIGKDD Int. Conf. Knowledge Discovery data Mining (Chicago, IL, USA), Aug. 2005, pp. 157-166.
|
19 |
H. V. Nguyen, H. H. Ang, and V. Gopalkrishnan, Mining outliers with ensemble of heterogeneous detectors on random subspaces, in Proc. Int. Conf. Database Syst. Adv. Appicat. (Tsukuba, Japan), 2010, pp. 368-383.
|
20 |
A. Zimek, R. J. G. B. Campello, and J. Sander, Ensembles for unsupervised outlier detection: Challenges and research questions, ACM SIGKDD Explorations 15 (2014), no. 1, 11-22.
DOI
|
21 |
Y. Zhao and M. K. Hryniewicki, DCSO: Dynamic combination of detector scores for outlier, Ensembles (2018), https://doi.org/10.13140/RG.2.2.11165.77288.
DOI
|
22 |
B. Micenkova, B. McWilliams, and I. Assent, Learning representations for outlier detection on a budget (BORE), arXiv Preprint: 1507.08104, 2015.
|
23 |
E. Schubert et al., On evaluation of outlier rankings and outlier scores, in Proc. SIAM Int. Conf. Data Mining (Anaheim, CA, USA), 2012, pp. 1047-1058.
|
24 |
Y. Freund and R. E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, J. Comput. Syst. Sci. 55 (1997), no. 1, 119-139.
DOI
|
25 |
D. H. Wolpert, Stacked generalization, Neural Netw. 5 (1992), no. 2, 241-259.
DOI
|
26 |
C. C. Aggarwal and S. Sathe, Outlier ensembles: An introduction, Springer, New York, NY, USA, 2017.
|
27 |
A. Klementiev, D. Roth, and K. Small, An unsupervised learning algorithm for rank aggregation, in Proc. Eur. Conf. Machine Learn. (Warsaw, Poland), 2007, pp. 616-623.
|
28 |
ODDS Library, 2016, [Available from: http://odds.cs.stonybrook.edu. last accessed December 2019].
|
29 |
E. M. Knorr and R. T. Ng, Algorithms for mining distance-based outliers in large dataset, in Proc. Int. Conf. Very Large Data Bases (New York, NY, USA), 1998, pp. 392-403.
|
30 |
J. Zhang, Towards outlier detection for high-dimensional data streams using projected outlier analysis strategy, Dissertation, Dalhousie University, Halifax, Canada, 2008.
|
31 |
H. P. Kriegel et al., Outlier detection in axis-parallel subspaces of high dimensional data, in Proc. Pacific-Asia Conf. Knowledge Discovery Data Mining (Bangkok, Thailand), 2009, pp. 831-838.
|
32 |
A. Emmott et al., A meta-analysis of the anomaly detection problem, arXiv preprint, arXiv:1503.01158, 2015.
|
33 |
J. Demsar, Statistical comparisons of classifiers over multiple data sets, J. Machine Learn. Research 7 (2006), 1-30.
|
34 |
L. Breiman, Bagging predictors, Machine Learn. 24 (1996), no. 2, 123-140.
DOI
|
35 |
V. Chandola, A. Banerjee, and V. Kumar, Anomaly detection: a survey, ACM Comput. Surveys 41 (2009), no. 3, 15:1-58.
|
36 |
E. Burnaev, P. Erofeev, and D. Smolyakov, Model selection for anomaly detection, in Proc. Int. Conf. Machine Vision (Barcelona, Spain), Oct. 2015, pp. 987525:1-6.
|
37 |
S. Ghosh and D. L. Reilly, Credit card fraud detection with a neural-network, in Proc. 27th Hawaii Int. Conf. Syst. Sci. (Wailea, HI, USA), Jan. 1994, pp. 621-630.
|
38 |
Y. Wang and R. Rekaya, LSOSS: Detection of cancer outlier differential gene expression, Biomarker Insights 5 (2010), 69-78.
|
39 |
C. C. Aggarwal, Outlier ensembles: position paper, ACM SIGKDD Explorations 14 (2013), no. 2, 49-58.
DOI
|
40 |
S. Das et al., Incorporating expert feedback into active anomaly discovery, in Proc. IEEE Int. Conf. Data Mining (Barcelona, Spain), Dec. 2016, pp. 853-858.
|
41 |
C. C. Aggarwal and S. Sathe, Theoretical foundations and algorithms for outlier ensembles, ACM SIGKDD Explorations Newsletter 17 (2015), no. 1, 24-47.
DOI
|
42 |
S. Rayana, W. Zhong, and L. Akoglu, Sequential ensemble learning for outlier detection: A bias-variance perspective, in Proc. IEEE Int. Conf. Data Mining (Barcelona, Spain), Dec 2016, pp. 1167-1172.
|
43 |
S. Rayana and L. Akoglu, Less is more: Building selective anomaly ensembles, Trans. Knowledge Discovery Data 10 (2016), no. 4, 1-33.
|
44 |
Y. Zhao and M. K. Hryniewicki, XGBOD: Improving supervised outlier detection with unsupervised representation learning, in Proc. Int. Joint Conf. Neural Netw. (Rio de Janeiro, Brazil), July 2018, pp. 1-8.
|
45 |
M. Xie et al., Anomaly detection in wireless sensor networks: a survey, J. Netw. Comput. Applicat. 34 (2011), no. 4, 1302-1325.
DOI
|
46 |
B. Wang and Z. Mao, Outlier detection based on a dynamic ensemble model: Applied to process monitoring, Inf. Fusion 51 (2019), 244-258.
DOI
|
47 |
M. N. Haque et al., Heterogeneous ensemble combination search using genetic algorithm for class imbalanced data, Classification, PLoS ONE 11 (2016), no. 1, e0146116:1-e146128.
|