Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Learning in the presence of concept drift and hidden contexts
Machine Learning
A robust algorithm for automatic extraction of an unknown number of clusters from noisy data
Pattern Recognition Letters
An on-line agglomerative clustering method for nonstationary data
Neural Computation
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Incremental learning in fuzzy pattern matching
Fuzzy Sets and Systems - Possibility theory and fuzzy logic
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A fuzzy controller with evolving structure
Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Bio-inspired systems (BIS)
Online classification of nonstationary data streams
Intelligent Data Analysis
Using cooperative mobile agents to monitor distributed and dynamic environments
Information Sciences: an International Journal
IKNN: Informative K-Nearest Neighbor Pattern Classification
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
A new monitoring design for uni-variate statistical quality control charts
Information Sciences: an International Journal
An extension of the naive Bayesian classifier
Information Sciences: an International Journal
Dynamic hierarchical algorithms for document clustering
Pattern Recognition Letters
Auto-adaptive and dynamical clustering neural network
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Information Sciences: an International Journal
Classification and Novel Class Detection in Concept-Drifting Data Streams under Time Constraints
IEEE Transactions on Knowledge and Data Engineering
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Editorial: Special Issue: Evolving Soft Computing Techniques and Applications
Applied Soft Computing
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The monitoring of a system functioning is achieved using a classifier which determines at each instant the class of a new incoming pattern. In non-stationary environments, the classifier must be able to adjust its parameters according to changes in the environment conditions. This requires a continuous learning while new patterns are available. Incremental learning is an efficient continuous learning technique for updating the classifier parameters without starting from scratch every time a new pattern is available. However in non-stationary environments, data characteristics may drift over time. This leads to deteriorate dramatically the performance of incremental learning algorithms over time. This is due to the use of data which is no more consistent with the characteristics of new incoming data. Thus, a mechanism to use only the recent and representative patterns to update the classifier parameters without a ''catastrophic forgetting'' is necessary. In this paper, we propose a dynamic pattern recognition method, named Dynamic Fuzzy Pattern Matching, to be used for the online monitoring of non-stationary processes functioning. This method is based on the use of an incremental algorithm allowing to follow the accumulated gradual changes of classes characteristics after the classification of each new pattern. When the accumulated gradual changes reach a suitable predefined threshold, the classifier parameters are adapted online using the recent and useful patterns.