On-line feature and classifier selection for agricultural produce

S. Laykin; Y. Edan; V. Alchanatis

On-line feature and classifier selection for agricultural produce

S. Laykin, Y. Edan, V. Alchanatis

Department of Industrial Engineering and Management

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper presents an on-line hierarchical classifier for agricultural products. The classifier consists of two levels. The first level detects new populations using an on-line clustering algorithm. The second level selects the best-fit classifier using a fuzzy system. This paper presents the combination of the two levels into a complete system. Feature selection is conducted on-line according to the classified population. A synthetic dataset is used to estimate the classifier capabilities and compare it to previous results. Results indicated that the combined online system results in improved classification accuracy.

Original language	English
Title of host publication	Proceedings of the Eighth IASTED International Conference On Artificial Intelligence and Soft Computing
Editors	A.P. Pobil
Pages	127-131
Number of pages	5
State	Published - 27 Dec 2004
Event	Proceedings of the Eighth IASTED International Conference on Atificial Intelligence and Soft Computing - Marbella, Spain Duration: 1 Sep 2004 → 3 Sep 2004

Publication series

Name	Proceedings of the Eighth IASTED International Conference on Artificial Intelligence and Soft Computing

Conference

Conference	Proceedings of the Eighth IASTED International Conference on Atificial Intelligence and Soft Computing
Country/Territory	Spain
City	Marbella
Period	1/09/04 → 3/09/04

Keywords

Classifier selection
Fuzzy rule-based system and feature selection
Unsupervised classification

ASJC Scopus subject areas

General Engineering

Cite this

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title = "On-line feature and classifier selection for agricultural produce",

abstract = "This paper presents an on-line hierarchical classifier for agricultural products. The classifier consists of two levels. The first level detects new populations using an on-line clustering algorithm. The second level selects the best-fit classifier using a fuzzy system. This paper presents the combination of the two levels into a complete system. Feature selection is conducted on-line according to the classified population. A synthetic dataset is used to estimate the classifier capabilities and compare it to previous results. Results indicated that the combined online system results in improved classification accuracy.",

keywords = "Classifier selection, Fuzzy rule-based system and feature selection, Unsupervised classification",

author = "S. Laykin and Y. Edan and V. Alchanatis",

year = "2004",

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day = "27",

language = "English",

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series = "Proceedings of the Eighth IASTED International Conference on Artificial Intelligence and Soft Computing",

pages = "127--131",

editor = "A.P. Pobil",

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note = "Proceedings of the Eighth IASTED International Conference on Atificial Intelligence and Soft Computing ; Conference date: 01-09-2004 Through 03-09-2004",

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Laykin, S, Edan, Y & Alchanatis, V 2004, On-line feature and classifier selection for agricultural produce. in AP Pobil (ed.), Proceedings of the Eighth IASTED International Conference On Artificial Intelligence and Soft Computing. Proceedings of the Eighth IASTED International Conference on Artificial Intelligence and Soft Computing, pp. 127-131, Proceedings of the Eighth IASTED International Conference on Atificial Intelligence and Soft Computing, Marbella, Spain, 1/09/04.

On-line feature and classifier selection for agricultural produce. / Laykin, S.; Edan, Y.; Alchanatis, V.
Proceedings of the Eighth IASTED International Conference On Artificial Intelligence and Soft Computing. ed. / A.P. Pobil. 2004. p. 127-131 (Proceedings of the Eighth IASTED International Conference on Artificial Intelligence and Soft Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - On-line feature and classifier selection for agricultural produce

AU - Laykin, S.

AU - Edan, Y.

AU - Alchanatis, V.

PY - 2004/12/27

Y1 - 2004/12/27

N2 - This paper presents an on-line hierarchical classifier for agricultural products. The classifier consists of two levels. The first level detects new populations using an on-line clustering algorithm. The second level selects the best-fit classifier using a fuzzy system. This paper presents the combination of the two levels into a complete system. Feature selection is conducted on-line according to the classified population. A synthetic dataset is used to estimate the classifier capabilities and compare it to previous results. Results indicated that the combined online system results in improved classification accuracy.

AB - This paper presents an on-line hierarchical classifier for agricultural products. The classifier consists of two levels. The first level detects new populations using an on-line clustering algorithm. The second level selects the best-fit classifier using a fuzzy system. This paper presents the combination of the two levels into a complete system. Feature selection is conducted on-line according to the classified population. A synthetic dataset is used to estimate the classifier capabilities and compare it to previous results. Results indicated that the combined online system results in improved classification accuracy.

KW - Classifier selection

KW - Fuzzy rule-based system and feature selection

KW - Unsupervised classification

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M3 - Conference contribution

AN - SCOPUS:10444283473

SN - 0889864586

T3 - Proceedings of the Eighth IASTED International Conference on Artificial Intelligence and Soft Computing

SP - 127

EP - 131

BT - Proceedings of the Eighth IASTED International Conference On Artificial Intelligence and Soft Computing

A2 - Pobil, A.P.

T2 - Proceedings of the Eighth IASTED International Conference on Atificial Intelligence and Soft Computing

Y2 - 1 September 2004 through 3 September 2004

ER -

On-line feature and classifier selection for agricultural produce

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

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