Introduction to algorithms
Support vector domain description
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
A minimum spanning tree algorithm with inverse-Ackermann type complexity
Journal of the ACM (JACM)
Two-phase clustering process for outliers detection
Pattern Recognition Letters
An optimal minimum spanning tree algorithm
Journal of the ACM (JACM)
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Discussion on the Classifier Projection Space for Classifier Combining
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Distance-based outliers: algorithms and applications
The VLDB Journal — The International Journal on Very Large Data Bases
Practical Parallel Algorithms for Minimum Spanning Trees
SRDS '98 Proceedings of the The 17th IEEE Symposium on Reliable Distributed Systems
Concept learning in the absence of counterexamples: an autoassociation-based approach to classification
One-class svms for document classification
The Journal of Machine Learning Research
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Authorship verification as a one-class classification problem
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A Consistency-Based Model Selection for One-Class Classification
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
One-class document classification via Neural Networks
Neurocomputing
From outliers to prototypes: Ordering data
Neurocomputing
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions
IEEE Transactions on Computers
On the History of the Minimum Spanning Tree Problem
IEEE Annals of the History of Computing
On optimum recognition error and reject tradeoff
IEEE Transactions on Information Theory
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Weighted bagging for graph based one-class classifiers
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
One-class classification with Gaussian processes
Pattern Recognition
Enhancing minimum spanning tree-based clustering by removing density-based outliers
Digital Signal Processing
Approximate polytope ensemble for one-class classification
Pattern Recognition
Diversity measures for one-class classifier ensembles
Neurocomputing
Active selection of clustering constraints: a sequential approach
Pattern Recognition
Clustering-based ensembles for one-class classification
Information Sciences: an International Journal
Authorship attribution as a case of anomaly detection: A neural network model
International Journal of Hybrid Intelligent Systems
Hi-index | 0.01 |
In the problem of one-class classification one of the classes, called the target class, has to be distinguished from all other possible objects. These are considered as non-targets. The need for solving such a task arises in many practical applications, e.g. in machine fault detection, face recognition, authorship verification, fraud recognition or person identification based on biometric data. This paper proposes a new one-class classifier, the minimum spanning tree class descriptor (MST_CD). This classifier builds on the structure of the minimum spanning tree constructed on the target training set only. The classification of test objects relies on their distances to the closest edge of that tree, hence the proposed method is an example of a distance-based one-class classifier. Our experiments show that the MST_CD performs especially well in case of small sample size problems and in high-dimensional spaces.