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
Local peculiarity factor and its application in outlier detection
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
One-Class Classification by Combining Density and Class Probability Estimation
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
An evaluation of dimension reduction techniques for one-class classification
Artificial Intelligence Review
ACM Computing Surveys (CSUR)
Mining Violations to Relax Relational Database Constraints
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
ODDC: outlier detection using distance distribution clustering
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
A hybrid fraud scoring and spike detection technique in streaming data
Intelligent Data Analysis
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Active learning and subspace clustering for anomaly detection
Intelligent Data Analysis
RKOF: robust kernel-based local outlier detection
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part II
iBAT: detecting anomalous taxi trajectories from GPS traces
Proceedings of the 13th international conference on Ubiquitous computing
Anomaly detection using ensembles
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Mining outliers with ensemble of heterogeneous detectors on random subspaces
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Isolation-Based Anomaly Detection
ACM Transactions on Knowledge Discovery from Data (TKDD)
Fast anomaly detection for streaming data
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Stratified k-means clustering over a deep web data source
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Unsupervised ensemble learning for mining top-n outliers
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Proceedings of the 5th ACM workshop on Security and artificial intelligence
A-GHSOM: An adaptive growing hierarchical self organizing map for network anomaly detection
Journal of Parallel and Distributed Computing
A learning system for discriminating variants of malicious network traffic
Proceedings of the Eighth Annual Cyber Security and Information Intelligence Research Workshop
Querying discriminative and representative samples for batch mode active learning
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Subsampling for efficient and effective unsupervised outlier detection ensembles
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition
One-class conditional random fields for sequential anomaly detection
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Ensembles for unsupervised outlier detection: challenges and research questions a position paper
ACM SIGKDD Explorations Newsletter
Research issues in outlier detection for data streams
ACM SIGKDD Explorations Newsletter
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Most existing approaches to outlier detection are based on density estimation methods. There are two notable issues with these methods: one is the lack of explanation for outlier flagging decisions, and the other is the relatively high computational requirement. In this paper, we present a novel approach to outlier detection based on classification, in an attempt to address both of these issues. Our approach isbased on two key ideas. First, we present a simple reduction of outlier detection to classification, via a procedure that involves applying classification to a labeled data set containing artificially generated examples that play the role of potential outliers. Once the task has been reduced to classification, we then invoke a selective sampling mechanism based on active learning to the reduced classification problem. We empirically evaluate the proposed approach using a number of data sets, and find that our method is superior to other methods based on the same reduction to classification, but using standard classification methods. We also show that it is competitive to the state-of-the-art outlier detection methods in the literature based on density estimation, while significantly improving the computational complexity and explanatory power.