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The Racing Algorithm: Model Selection for Lazy Learners
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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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Anomaly Detection over Noisy Data using Learned Probability Distributions
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Adaptive spike detection for resilient data stream mining
AusDM '07 Proceedings of the sixth Australasian conference on Data mining and analytics - Volume 70
On the chance accuracies of large collections of classifiers
Proceedings of the 25th international conference on Machine learning
Anomaly pattern detection in categorical datasets
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ACM Computing Surveys (CSUR)
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IEEE Transactions on Intelligent Transportation Systems
Real-time driving danger-level prediction
Engineering Applications of Artificial Intelligence
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ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part II
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ACM Transactions on Sensor Networks (TOSN)
Real-valued all-dimensions search: low-overhead rapid searching over subsets of attributes
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Mining outliers in spatial networks
DASFAA'06 Proceedings of the 11th international conference on Database Systems for Advanced Applications
Ranking outliers using symmetric neighborhood relationship
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
A minimum spanning tree-inspired clustering-based outlier detection technique
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
Hidden Source Behavior Change Tracking and Detection
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
Mining multidimensional contextual outliers from categorical relational data
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Review: A review of novelty detection
Signal Processing
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This paper presents an algorithm for performing early detection of disease outbreaks by searching a database of emergency department cases for anomalous patterns. Traditional techniques for anomaly detection are unsatisfactory for this problem because they identify individual data points that are rare due to particular combinations of features. When applied to our scenario, these traditional algorithms discover isolated outliers of particularly strange events, such as someone accidentally shooting their ear, that are not indicative of a new outbreak. Instead, we would like to detect anomalous patterns. These patterns are groups with specific characteristics whose recent pattern of illness is anomalous relative to historical patterns. We propose using a rule-based anomaly detection algorithm that characterizes each anomalous pattern with a rule. The significance of each rule is carefully evaluated using Fisher's Exact Test and a randomization test. Our algorithm is compared against a standard detection algorithm by measuring the number of false positives and the timeliness of detection. Simulated data, produced by a simulator that creates the effects of an epidemic on a city, is used for evaluation. The results indicate that our algorithm has significantly better detection times for common significance thresholds while having a slightly higher false positive rate.