Abstract
This paper presents an on-line multi-stage sorting algorithm capable of adapting to different populations. The sorting algorithm selects on-line the most appropriate classifier and feature subsets for the incoming population. The sorting algorithm includes two levels, a low level for population detection and a high level for classifier selection which incorporates feature selection. Population detection is achieved by an on-line unsupervised clustering algorithm that analyzes product variability. The classifier selection uses n fuzzy kNN classifiers, each trained with different feature combinations that function as input to a fuzzy rule-based decision system. Re-training of the n fuzzy kNN classifiers occurs when the rule based system cannot assign an existing classifier with high confidence level. Classification results for synthetic and real world databases are presented.
Original language | American English |
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Pages (from-to) | 2843-2853 |
Number of pages | 11 |
Journal | Pattern Recognition |
Volume | 45 |
Issue number | 7 |
DOIs | |
State | Published - 1 Jul 2012 |
Keywords
- Classification
- Classifier selection
- Feature selection
- Fuzzy logic
All Science Journal Classification (ASJC) codes
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Artificial Intelligence