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.

► On-line multi-stage sorting algorithm capable of adapting to different populations.
► One level for population detection and another level for classifier selection.
► Population detection is achieved by an on-line unsupervised clustering algorithm.
► Classifier selection using fuzzy kNN classifiers, trained with different features.
► The fuzzy kNN classifiers are retrained when an existing classifier is not assigned.