On-line multi-stage sorting algorithm for agriculture products

Victor Alchanatis, Yael Edan

Research output: Contribution to journalArticlepeer-review

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 languageAmerican English
Pages (from-to)2843-2853
Number of pages11
JournalPattern Recognition
Volume45
Issue number7
DOIs
StatePublished - 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

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