On the combination of support vector machines and segmentation algorithms for anomaly detection: A petroleum industry comparative study

Barbara, 2001, Detecting novel network intrusions using Bayes estimators Bingham, 2006, Segmentation and dimensionality reduction, 372 Bollobás, 1997, Time-series similarity problems and well-separated geometric sets, 454 Borrajo, 2011, Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises, Int. J. Neural Syst., 21, 277, 10.1142/S0129065711002833 Breunig, 2000, LOF: identifying density-based local outliers, 93 Calvo-Rolle, 2014, A bio-inspired knowledge system for improving combined cycle plant control tuning, Neurocomputing, 126, 95, 10.1016/j.neucom.2013.01.055 Chambers, 1983 Chandola, 2009, Anomaly detection: a survey, ACM Comput. Surv., 41, 15, 10.1145/1541880.1541882 Di Eugenio, 2004, The Kappa statistic: a second look, Comput. Linguist., 30, 95, 10.1162/089120104773633402 Eskin, 2002, A geometric framework for unsupervised anomaly detection, 77 Fujimaki, 2005, An approach to spacecraft anomaly detection problem using kernel feature space, 401 Grubbs, 1969, Procedures for detecting outlying observations in samples, Technometrics, 11, 1, 10.1080/00401706.1969.10490657 Hunter, 1999, Knowledge-based event detection in complex time series data, 271 Kalman, 1960, A new approach to linear filtering and prediction problems, J. Basic Eng., 82, 35, 10.1115/1.3662552 Keogh, 2002, Finding surprising patterns in a time series database in linear time and space, 550 King, 2002, The use of novelty detection techniques for monitoring high-integrity plant, 221 Lemire, 2007, A better alternative to piecewise linear time series segmentation Martí, 2011 Martí, 2014, YASA: yet another time series segmentation algorithm for anomaly detection in big data problems, 697 Martí, 2015, Anomaly detection based on sensor data in petroleum industry applications, Sensors, 15, 2774, 10.3390/s150202774 Maybeck, 1979, Stochastic Models, Estimation, and Control, vol. 141 McNemar, 1947, Note on the sampling error of the difference between correlated proportions or percentages, Psychometrika, 12, 153, 10.1007/BF02295996 Moya, 1996, Network constraints and multi-objective optimization for one-class classification, Neural Netw., 9, 463, 10.1016/0893-6080(95)00120-4 Neyman, 1937, Outline of a theory of statistical estimation based on the classical theory of probability, Philos. Trans. R. Soc. A, 236, 333 Papadimitriou, 2003, LOCI: fast outlier detection using the local correlation integral, 315 Ratsch, 2002, Constructing boosting algorithms from SVMs: an application to one-class classification, IEEE Trans. Pattern Anal. Mach. Intell., 24, 1184, 10.1109/TPAMI.2002.1033211 Ringberg, 2007, Sensitivity of PCA for traffic anomaly detection, vol. 35, 109 Roth, 2005, Outlier detection with one-class kernel Fisher discriminants, vol. 17, 1169 Salzberg, 1997, On comparing classifiers: pitfalls to avoid and a recommended approach, Data Min. Knowl. Discov., 1, 317, 10.1023/A:1009752403260 Sanchez-Pi, 2014, High-level information fusion for risk and accidents prevention in pervasive oil industry environments, 202 Schölkopf, 1999, Support vector method for novelty detection, vol. 12, 582 Shuai, 2008, A Kalman filter based approach for outlier detection in sensor networks, 154 Ting, 2007, A Kalman filter for robust outlier detection, 1514 Vlachos, 2003, A wavelet-based anytime algorithm for k-means clustering of time series Woźniak, 2014, A survey of multiple classifier systems as hybrid systems, Inf. Fusion, 16, 3, 10.1016/j.inffus.2013.04.006