2010 MultipleKernelLearningforHetero

• (Das et al., 2010) ⇒ Santanu Das, Bryan L. Matthews, Ashok N. Srivastava, and Nikunj C. Oza. (2010). “Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835813

Subject Headings: Aviation Safety.

Cited By

• http://scholar.google.com/scholar?q=%22Multiple+kernel+learning+for+heterogeneous+anomaly+detection%3A+algorithm+and+aviation+safety+case+study%22+2010
• http://portal.acm.org/citation.cfm?id=1835813&preflayout=flat#citedby

Quotes

Abstract

The world-wide aviation system is one of the most complex dynamical systems ever developed and is generating data at an extremely rapid rate. Most modern commercial aircraft record several hundred flight parameters including information from the guidance, navigation, and control systems, the avionics and propulsion systems, and the pilot inputs into the aircraft. These parameters may be continuous measurements or binary or categorical measurements recorded in one second intervals for the duration of the flight. Currently, most approaches to aviation safety are reactive, meaning that they are designed to react to an aviation safety incident or accident. In this paper, we discuss a novel approach based on the theory of multiple kernel learning to detect potential safety anomalies in very large data bases of discrete and continuous data from world-wide operations of commercial fleets. We pose a general anomaly detection problem which includes both discrete and continuous data streams, where we assume that the discrete streams have a causal influence on the continuous streams. We also assume that atypical sequences of events in the discrete streams can lead to off-nominal system performance. We discuss the application domain, novel algorithms, and also discuss results on real-world data sets. Our algorithm uncovers operationally significant events in high dimensional data streams in the aviation industry which are not detectable using state of the art methods.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 MultipleKernelLearningforHetero	Santanu Das Bryan L. Matthews Ashok N. Srivastava Nikunj C. Oza			Multiple Kernel Learning for Heterogeneous Anomaly Detection: Algorithm and Aviation Safety Case Study		KDD-2010 Proceedings		10.1145/1835804.1835813		2010