Algorithms for clustering data
Algorithms for clustering data
C4.5: programs for machine learning
C4.5: programs for machine learning
Hierarchical classification of surface defects on dusty wood boards
Pattern Recognition Letters
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Scale & Affine Invariant Interest Point Detectors
International Journal of Computer Vision
Probabilistic Tracking with Adaptive Feature Selection
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Improving Nearest Neighbor Classifier Using Tabu Search and Ensemble Distance Metrics
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Pattern Recognition Letters
Adaptive surface inspection via interactive evolution
Image and Vision Computing
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Extensions of vector quantization for incremental clustering
Pattern Recognition
Diversity-Based Classifier Selection for Adaptive Object Tracking
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Human-machine interaction issues in quality control based on online image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Towards incremental classifier fusion
Intelligent Data Analysis
Creating diverse nearest-neighbour ensembles using simultaneous metaheuristic feature selection
Pattern Recognition Letters
SparseFIS: data-driven learning of fuzzy systems with sparsity constraints
IEEE Transactions on Fuzzy Systems
On dynamic soft dimension reduction in evolving fuzzy classifiers
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
On-line incremental feature weighting in evolving fuzzy classifiers
Fuzzy Sets and Systems
Handling drifts and shifts in on-line data streams with evolving fuzzy systems
Applied Soft Computing
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In this paper we present a novel image classification framework, which is able to automatically re-configure and adapt its featuredriven classifiers and improve its performance based on user interaction during on-line processing mode. Special emphasis is placed on the generic applicability of the framework to arbitrary surface inspection systems. The basic components of the framework include: recognition of regions of interest (objects), adaptive feature extraction, dealing with hierarchical information in classification, initial batch training with redundancy deletion and feature selection components, on-line adaptation and refinement of the classifiers based on operators' feedback, and resolving contradictory inputs from several operators by ensembling outputs from different individual classifiers. The paper presents an outline on each of these components and concludes with a thorough discussion of basic and improved off-line and on-line classification results for artificial data sets and real-world images recorded during a CD imprint production process.