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
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Fast Outlier Detection in High Dimensional Spaces
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Outlier Detection Using Replicator Neural Networks
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Enhancing Effectiveness of Outlier Detections for Low Density Patterns
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Discovering cluster-based local outliers
Pattern Recognition Letters
Approximations to Magic: Finding Unusual Medical Time Series
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Estimating the Support of a High-Dimensional Distribution
Neural Computation
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Minimum Enclosing and Maximum Excluding Machine for Pattern Description and Discrimination
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
ACM Computing Surveys (CSUR)
Robust support vector machine training via convex outlier ablation
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
LoOP: local outlier probabilities
Proceedings of the 18th ACM conference on Information and knowledge management
Analysis of Multi-stage Convex Relaxation for Sparse Regularization
The Journal of Machine Learning Research
Some properties of the Gaussian kernel for one class learning
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Distortion Measurement for Automatic Document Verification
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
Ranking outliers using symmetric neighborhood relationship
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Robust support vector machine with bullet hole image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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Support Vector Machines (SVMs) have been one of the most successful machine learning techniques for the past decade. For anomaly detection, also a semi-supervised variant, the one-class SVM, exists. Here, only normal data is required for training before anomalies can be detected. In theory, the one-class SVM could also be used in an unsupervised anomaly detection setup, where no prior training is conducted. Unfortunately, it turns out that a one-class SVM is sensitive to outliers in the data. In this work, we apply two modifications in order to make one-class SVMs more suitable for unsupervised anomaly detection: Robust one-class SVMs and eta one-class SVMs. The key idea of both modifications is, that outliers should contribute less to the decision boundary as normal instances. Experiments performed on datasets from UCI machine learning repository show that our modifications are very promising: Comparing with other standard unsupervised anomaly detection algorithms, the enhanced one-class SVMs are superior on two out of four datasets. In particular, the proposed eta one-class SVM has shown the most promising results.