COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Learning distributions by their density levels: a paradigm for learning without a teacher
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Efficient algorithms for mining outliers from large data sets
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Query Learning Strategies Using Boosting and Bagging
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Efficient Mining from Large Databases by Query Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Using Artificial Anomalies to Detect Unknown and Known Network Intrusions
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Parzen-Window Network Intrusion Detectors
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Diverse ensembles for active learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Feature bagging for outlier detection
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Finding anomalous periodic time series
Machine Learning
ACM Computing Surveys (CSUR)
Isolation-Based Anomaly Detection
ACM Transactions on Knowledge Discovery from Data (TKDD)
Similarity kernels for nearest neighbor-based outlier detection
IDA'10 Proceedings of the 9th international conference on Advances in Intelligent Data Analysis
Stratified k-means clustering over a deep web data source
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Continuous adaptive outlier detection on distributed data streams
HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
Approximate document outlier detection using random spectral projection
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Review: A review of novelty detection
Signal Processing
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An effective approach to detecting anomalous points in a data setis distance-based outlier detection. This paper describes a simplesampling algorithm to effciently detect distance-based outliers indomains where each and every distance computation is veryexpensive. Unlike any existing algorithms, the sampling algorithmrequires a xed number of distance computations and can return goodresults with accuracy guarantees. The most computationallyexpensive aspect of estimating the accuracy of the result issorting all of the distances computed by the sampling algorithm.The experimental study on two expensive domains as well as tenadditional real-life datasets demonstrates both the effciency andeffectiveness of the sampling algorithm in comparison with thestate-of-the-art algorithm and there liability of the accuracyguarantees.