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Pattern Recognition
Laykin, S., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization, Bet Dagan 50250, Israel
Edan, Y., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
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. © 2011 Elsevier Ltd. All rights reserved.
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On-line multi-stage sorting algorithm for agriculture products
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Laykin, S., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
Alchanatis, V., Institute of Agricultural Engineering, Agricultural Research Organization, Bet Dagan 50250, Israel
Edan, Y., Department of Industrial Engineering and Management, Ben-Gurion University of the Negev, Beer Sheva 84105, Israel
On-line multi-stage sorting algorithm for agriculture products
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. © 2011 Elsevier Ltd. All rights reserved.
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