Design of a two-stage fuzzy classification model

Tzuu-Hseng S. Li, Nai Ren Guo, Chia Ping Cheng

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

This paper proposes a novel two-stage fuzzy classification model established by the fuzzy feature extraction agent (FFEA) and the fuzzy classification unit (FCU). At first, we propose a FFEA to validly extraction the feature variables from the original database. And then, the FCU, which is the main determination of the classification result, is developed to generate the if-then rules automatically. In fact, both the FFEA and FCU are fuzzy models themselves. In order to obtain better classification results, we utilize the genetic algorithms (GAs) and adaptive grade mechanism (AGM) to tune the FFEA and FCU, respectively, to improve the performance of the proposed fuzzy classification model. In this model, GAs are used to determine the distribution of the fuzzy sets for each feature variable of the FFEA, and the AGM is developed to regulate the confidence grade of the principal if-then rule of the FCU. Finally, the well-known Iris, Wine, and Glass databases are exploited to test the performances. Computer simulation results demonstrate that the proposed fuzzy classification model can provide a sufficiently high classification rate in comparison with other models in the literature.

Original languageEnglish
Pages (from-to)1482-1495
Number of pages14
JournalExpert Systems With Applications
Volume35
Issue number3
DOIs
Publication statusPublished - 2008 Oct 1

Fingerprint

Feature extraction
Genetic algorithms
Wine
Fuzzy sets
Glass
Computer simulation

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications

Cite this

Li, Tzuu-Hseng S. ; Guo, Nai Ren ; Cheng, Chia Ping. / Design of a two-stage fuzzy classification model. In: Expert Systems With Applications. 2008 ; Vol. 35, No. 3. pp. 1482-1495.
@article{2c56cdb3b3d54d9ab70c3dd874668f1b,
title = "Design of a two-stage fuzzy classification model",
abstract = "This paper proposes a novel two-stage fuzzy classification model established by the fuzzy feature extraction agent (FFEA) and the fuzzy classification unit (FCU). At first, we propose a FFEA to validly extraction the feature variables from the original database. And then, the FCU, which is the main determination of the classification result, is developed to generate the if-then rules automatically. In fact, both the FFEA and FCU are fuzzy models themselves. In order to obtain better classification results, we utilize the genetic algorithms (GAs) and adaptive grade mechanism (AGM) to tune the FFEA and FCU, respectively, to improve the performance of the proposed fuzzy classification model. In this model, GAs are used to determine the distribution of the fuzzy sets for each feature variable of the FFEA, and the AGM is developed to regulate the confidence grade of the principal if-then rule of the FCU. Finally, the well-known Iris, Wine, and Glass databases are exploited to test the performances. Computer simulation results demonstrate that the proposed fuzzy classification model can provide a sufficiently high classification rate in comparison with other models in the literature.",
author = "Li, {Tzuu-Hseng S.} and Guo, {Nai Ren} and Cheng, {Chia Ping}",
year = "2008",
month = "10",
day = "1",
doi = "10.1016/j.eswa.2007.08.045",
language = "English",
volume = "35",
pages = "1482--1495",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Limited",
number = "3",

}

Design of a two-stage fuzzy classification model. / Li, Tzuu-Hseng S.; Guo, Nai Ren; Cheng, Chia Ping.

In: Expert Systems With Applications, Vol. 35, No. 3, 01.10.2008, p. 1482-1495.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Design of a two-stage fuzzy classification model

AU - Li, Tzuu-Hseng S.

AU - Guo, Nai Ren

AU - Cheng, Chia Ping

PY - 2008/10/1

Y1 - 2008/10/1

N2 - This paper proposes a novel two-stage fuzzy classification model established by the fuzzy feature extraction agent (FFEA) and the fuzzy classification unit (FCU). At first, we propose a FFEA to validly extraction the feature variables from the original database. And then, the FCU, which is the main determination of the classification result, is developed to generate the if-then rules automatically. In fact, both the FFEA and FCU are fuzzy models themselves. In order to obtain better classification results, we utilize the genetic algorithms (GAs) and adaptive grade mechanism (AGM) to tune the FFEA and FCU, respectively, to improve the performance of the proposed fuzzy classification model. In this model, GAs are used to determine the distribution of the fuzzy sets for each feature variable of the FFEA, and the AGM is developed to regulate the confidence grade of the principal if-then rule of the FCU. Finally, the well-known Iris, Wine, and Glass databases are exploited to test the performances. Computer simulation results demonstrate that the proposed fuzzy classification model can provide a sufficiently high classification rate in comparison with other models in the literature.

AB - This paper proposes a novel two-stage fuzzy classification model established by the fuzzy feature extraction agent (FFEA) and the fuzzy classification unit (FCU). At first, we propose a FFEA to validly extraction the feature variables from the original database. And then, the FCU, which is the main determination of the classification result, is developed to generate the if-then rules automatically. In fact, both the FFEA and FCU are fuzzy models themselves. In order to obtain better classification results, we utilize the genetic algorithms (GAs) and adaptive grade mechanism (AGM) to tune the FFEA and FCU, respectively, to improve the performance of the proposed fuzzy classification model. In this model, GAs are used to determine the distribution of the fuzzy sets for each feature variable of the FFEA, and the AGM is developed to regulate the confidence grade of the principal if-then rule of the FCU. Finally, the well-known Iris, Wine, and Glass databases are exploited to test the performances. Computer simulation results demonstrate that the proposed fuzzy classification model can provide a sufficiently high classification rate in comparison with other models in the literature.

UR - http://www.scopus.com/inward/record.url?scp=44949252086&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=44949252086&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2007.08.045

DO - 10.1016/j.eswa.2007.08.045

M3 - Article

VL - 35

SP - 1482

EP - 1495

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 3

ER -