A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Parzen-Window Network Intrusion Detectors
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Novelty detection: a review—part 1: statistical approaches
Signal Processing
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Novelty detection with application to data streams
Intelligent Data Analysis - Knowledge Discovery from Data Streams
The use of features selection and nearest neighbors rule for faults diagnostic in induction motors
Engineering Applications of Artificial Intelligence
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
The Journal of Machine Learning Research
Efficiency issues of evolutionary k-means
Applied Soft Computing
Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints
IEEE Transactions on Knowledge and Data Engineering
Support vector machine in novelty detection for multi-channel combustion data
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Detecting Recurring and Novel Classes in Concept-Drifting Data Streams
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
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Novelty detection has been presented in the literature as one-class problem. In this case, new examples are classified as either belonging to the target class or not. The examples not explained by the model are detected as belonging to a class named novelty. However, novelty detection is much more general, especially in data streams scenarios, where the number of classes might be unknown before learning and new classes can appear any time. In this case, the novelty concept is composed by different classes. This work presents a new algorithm to address novelty detection in data streams multi-class problems, the MINAS algorithm. Moreover, we also present a new experimental methodology to evaluate novelty detection methods in multi-class problems. The data used in the experiments include artificial and real data sets. Experimental results show that MINAS is able to discover novelties in multi-class problems.