On-line multi-stage sorting algorithm for agriculture products

On-line multi-stage sorting algorithm for agriculture products: Publication year: 2012
Source: Pattern Recognition, Available online 12 January 2012
Shahar Laykin, Victor Alchanatis, Yael Edan
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 usesnfuzzy kNN classifiers, each trained with different feature combinations that function as input to a fuzzy rule-based decision system. Re-training of thenfuzzy 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.

Highlights

► 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.