The Self-organizing map as a tool in knowledge engineering
Pattern recognition in soft computing paradigm
Outlier Detection Using Replicator Neural Networks
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Online novelty detection on temporal sequences
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
An Adaptive Learning Approach for Noisy Data Streams
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
TAO-robust backpropagation learning algorithm
Neural Networks
Robust MCD-Based Backpropagation Learning Algorithm
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Time Series Clustering for Anomaly Detection Using Competitive Neural Networks
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Fast robust learning algorithm dedicated to LMLS criterion
ICAISC'10 Proceedings of the 10th international conference on Artifical intelligence and soft computing: Part II
Robust error measure for supervised neural network learning with outliers
IEEE Transactions on Neural Networks
The annealing robust backpropagation (ARBP) learning algorithm
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In the on-line data processing it is important to detect a novelty as soon as it appears, because it may be a consequence of gross errors or sudden change in the analysed system. In this paper we present a framework of novelty detection, based on the robust neural network. To detect novel patterns we compare responses of two autoregressive neural networks. One of them is trained with a robust learning algorithm designed to remove the influence of outliers, while the other uses simple training, based on the least squares error criterion. We present also a simple and easy to use approach that adapts this technique to data streams. Experiments conducted on data containing novelty and outliers have shown promising performance of the new method, applied to analyse temporal sequences.