Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications

Jyun Yu Jhang; Chin Ling Lee; Cheng Jian Lin; Chin Teng Lin; Kuu-Young Young

doi:10.1051/matecconf/201820105004

Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications

Jyun Yu Jhang, Chin Ling Lee, Cheng Jian Lin^*, Chin Teng Lin, Kuu-Young Young

^*Corresponding author for this work

Department of Electrical and Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

In this study, two intelligent classifiers, the AdaBoost-based incremental functional neural fuzzy classifier (AIFNFC) and the AdaBoost-based fixed functional neural fuzzy classifier (AFFNFC), are proposed for solving the classification problems. The AIFNFC approach will increase the amount of functional neural fuzzy classifiers based on the corresponding error during the training phase; while the AFNFC approach is equipped with a fixed amount of functional neural fuzzy classifiers. Then, the weights of AdaBoost procedure are assigned for classifiers. The proposed methods are applied to different classification benchmarks. Results of this study demonstrate the effectiveness of the proposed AIFNFC and AFFNFC methods.

Original language	English
Article number	05004
Journal	MATEC Web of Conferences
Volume	201
DOIs	https://doi.org/10.1051/matecconf/201820105004
State	Published - 14 Sep 2018
Event	3rd International Conference on Inventions, ICI 2017 - Sun Moon Lake, Taiwan Duration: 29 Sep 2017 → 2 Oct 2017

Access to Document

10.1051/matecconf/201820105004

Cite this

@article{9ede18a6994e4268b5943622cfad37aa,

title = "Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications",

abstract = "In this study, two intelligent classifiers, the AdaBoost-based incremental functional neural fuzzy classifier (AIFNFC) and the AdaBoost-based fixed functional neural fuzzy classifier (AFFNFC), are proposed for solving the classification problems. The AIFNFC approach will increase the amount of functional neural fuzzy classifiers based on the corresponding error during the training phase; while the AFNFC approach is equipped with a fixed amount of functional neural fuzzy classifiers. Then, the weights of AdaBoost procedure are assigned for classifiers. The proposed methods are applied to different classification benchmarks. Results of this study demonstrate the effectiveness of the proposed AIFNFC and AFFNFC methods.",

author = "Jhang, {Jyun Yu} and Lee, {Chin Ling} and Lin, {Cheng Jian} and Lin, {Chin Teng} and Kuu-Young Young",

note = "Publisher Copyright: {\textcopyright} The Authors, published by EDP Sciences, 2018.; 3rd International Conference on Inventions, ICI 2017 ; Conference date: 29-09-2017 Through 02-10-2017",

year = "2018",

month = sep,

day = "14",

doi = "10.1051/matecconf/201820105004",

language = "English",

volume = "201",

journal = "MATEC Web of Conferences",

issn = "2261-236X",

publisher = "EDP Sciences",

}

TY - JOUR

T1 - Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications

AU - Jhang, Jyun Yu

AU - Lee, Chin Ling

AU - Lin, Cheng Jian

AU - Lin, Chin Teng

AU - Young, Kuu-Young

N1 - Publisher Copyright: © The Authors, published by EDP Sciences, 2018.

PY - 2018/9/14

Y1 - 2018/9/14

N2 - In this study, two intelligent classifiers, the AdaBoost-based incremental functional neural fuzzy classifier (AIFNFC) and the AdaBoost-based fixed functional neural fuzzy classifier (AFFNFC), are proposed for solving the classification problems. The AIFNFC approach will increase the amount of functional neural fuzzy classifiers based on the corresponding error during the training phase; while the AFNFC approach is equipped with a fixed amount of functional neural fuzzy classifiers. Then, the weights of AdaBoost procedure are assigned for classifiers. The proposed methods are applied to different classification benchmarks. Results of this study demonstrate the effectiveness of the proposed AIFNFC and AFFNFC methods.

AB - In this study, two intelligent classifiers, the AdaBoost-based incremental functional neural fuzzy classifier (AIFNFC) and the AdaBoost-based fixed functional neural fuzzy classifier (AFFNFC), are proposed for solving the classification problems. The AIFNFC approach will increase the amount of functional neural fuzzy classifiers based on the corresponding error during the training phase; while the AFNFC approach is equipped with a fixed amount of functional neural fuzzy classifiers. Then, the weights of AdaBoost procedure are assigned for classifiers. The proposed methods are applied to different classification benchmarks. Results of this study demonstrate the effectiveness of the proposed AIFNFC and AFFNFC methods.

UR - http://www.scopus.com/inward/record.url?scp=85054321001&partnerID=8YFLogxK

U2 - 10.1051/matecconf/201820105004

DO - 10.1051/matecconf/201820105004

M3 - Conference article

AN - SCOPUS:85054321001

SN - 2261-236X

VL - 201

JO - MATEC Web of Conferences

JF - MATEC Web of Conferences

M1 - 05004

T2 - 3rd International Conference on Inventions, ICI 2017

Y2 - 29 September 2017 through 2 October 2017

ER -

Using AdaBoost-based Multiple Functional Neural Fuzzy Classifiers Fusion for Classification Applications

Abstract

Access to Document

Other files and links

Fingerprint

Cite this