Incremental Learning from Noisy Data
Machine Learning
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric discriminant analysis and nearest neighbor classification
Pattern Recognition Letters
Online Handwritten Script Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An adaptive k-nearest neighbor text categorization strategy
ACM Transactions on Asian Language Information Processing (TALIP)
Nearest-neighbor automatic sound annotation with a WordNet taxonomy
Journal of Intelligent Information Systems - Special issue: Intelligent multimedia applications
Using multiple windows to track concept drift
Intelligent Data Analysis
Dynamic integration of classifiers for handling concept drift
Information Fusion
Top 10 algorithms in data mining
Knowledge and Information Systems
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Categorizing and mining concept drifting data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
An experimental comparison of performance measures for classification
Pattern Recognition Letters
Issues in evaluation of stream learning algorithms
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Similarity-based Classification: Concepts and Algorithms
The Journal of Machine Learning Research
Combining Time and Space Similarity for Small Size Learning under Concept Drift
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
An adaptable k-nearest neighbors algorithm for MMSE image interpolation
IEEE Transactions on Image Processing
Tracking recurring contexts using ensemble classifiers: an application to email filtering
Knowledge and Information Systems
Algorithms and networks for accelerated convergence of adaptive LDA
Pattern Recognition
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Software refactoring at the function level using new Adaptive K-Nearest Neighbor algorithm
Advances in Engineering Software
Using web sources for improving video categorization
Journal of Intelligent Information Systems
Change with Delayed Labeling: When is it Detectable?
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
A Survey of Outlier Detection Methods in Network Anomaly Identification
The Computer Journal
Combining similarity in time and space for training set formation under concept drift
Intelligent Data Analysis
Real-Time On-Demand Motion Video Change Detection in the Sensor Web Environment
The Computer Journal
Just-in-Time Adaptive Classifiers—Part II: Designing the Classifier
IEEE Transactions on Neural Networks
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This article introduces a just-in-time adaptive nonparametric multiclass component analysis technique for application in nonstationary environments. This generative model enables adaptive similarity-based classifiers to classify time-labeled inquiry patterns with superior accuracy in low-dimensional feature space. While there are adaptive forms of feature extraction methods, which transform training patterns to low-dimensional space and/or improve classifier accuracy, they are vulnerable to nonparametric changes in data and must continuously update their parameters. In the proposed method, an optimal transformation matrix transforms time-labeled instances from the original space to a new feature space to maximize the probability of selecting the correct class label for incoming instances using similarity-based classifiers. To this end, for a given time-labeled instance, nonparametric intra-class and extra-class distributions are proposed. The proposed method is also furnished to a temporal detector to provide the most convenient time for the adaptation phase. Experimental results on real and synthesized datasets that include real and artificial changes demonstrate the performance of the proposed method in terms of accuracy and dimension reduction in dynamic environments.