Fuzzy rules generation method for classification problems using rough sets and genetic algorithms

Marek Sikora

Fuzzy rules generation method for classification problems using rough sets and genetic algorithms

Marek Sikora

Department of Computer Networks and Systems (RAU/RAU9)

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

Abstract

A method of constructing a classifier that uses fuzzy reasoning is described in this paper. Rules for this classifier are obtained by means of algorithms relying on a tolerance rough sets model. Got rules are in so called sharp" form, a genetic algorithm is used for fuzzification of these rules. Presented results of experiments show that the proposed method allows getting a smaller rules set with similar (or better) classification abilities.

Original language	English
Title of host publication	Proceedings of SPIE - The International Society for Optical Engineering
Pages	383-391
Number of pages	9
Publication status	Published - 2006
Event	High-Power Diode Laser Technology and Applications IV - San Jose, CA, United States Duration: 23 Jan 2006 → 25 Jan 2006

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	6104
ISSN (Print)	0277-786X

Conference

Conference	High-Power Diode Laser Technology and Applications IV
Country/Territory	United States
City	San Jose, CA
Period	23/01/06 → 25/01/06

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Cite this

@inproceedings{b851a26b3a0647e4b2ba062d1b7c53e3,

title = "Fuzzy rules generation method for classification problems using rough sets and genetic algorithms",

abstract = "A method of constructing a classifier that uses fuzzy reasoning is described in this paper. Rules for this classifier are obtained by means of algorithms relying on a tolerance rough sets model. Got rules are in so called sharp{"} form, a genetic algorithm is used for fuzzification of these rules. Presented results of experiments show that the proposed method allows getting a smaller rules set with similar (or better) classification abilities.",

author = "Marek Sikora",

year = "2006",

language = "English",

isbn = "0819461466",

series = "Proceedings of SPIE - The International Society for Optical Engineering",

pages = "383--391",

booktitle = "Proceedings of SPIE - The International Society for Optical Engineering",

note = "High-Power Diode Laser Technology and Applications IV ; Conference date: 23-01-2006 Through 25-01-2006",

}

Sikora, M 2006, Fuzzy rules generation method for classification problems using rough sets and genetic algorithms. in Proceedings of SPIE - The International Society for Optical Engineering. Proceedings of SPIE - The International Society for Optical Engineering, vol. 6104, pp. 383-391, High-Power Diode Laser Technology and Applications IV, San Jose, CA, United States, 23/01/06.

Fuzzy rules generation method for classification problems using rough sets and genetic algorithms. / Sikora, Marek.
Proceedings of SPIE - The International Society for Optical Engineering. 2006. p. 383-391 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 6104).

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

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PY - 2006

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N2 - A method of constructing a classifier that uses fuzzy reasoning is described in this paper. Rules for this classifier are obtained by means of algorithms relying on a tolerance rough sets model. Got rules are in so called sharp" form, a genetic algorithm is used for fuzzification of these rules. Presented results of experiments show that the proposed method allows getting a smaller rules set with similar (or better) classification abilities.

AB - A method of constructing a classifier that uses fuzzy reasoning is described in this paper. Rules for this classifier are obtained by means of algorithms relying on a tolerance rough sets model. Got rules are in so called sharp" form, a genetic algorithm is used for fuzzification of these rules. Presented results of experiments show that the proposed method allows getting a smaller rules set with similar (or better) classification abilities.

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M3 - Conference contribution

AN - SCOPUS:33645969617

SN - 0819461466

SN - 9780819461469

T3 - Proceedings of SPIE - The International Society for Optical Engineering

SP - 383

EP - 391

BT - Proceedings of SPIE - The International Society for Optical Engineering

T2 - High-Power Diode Laser Technology and Applications IV

Y2 - 23 January 2006 through 25 January 2006

ER -

Fuzzy rules generation method for classification problems using rough sets and genetic algorithms

Abstract

Publication series

Conference

ASJC Scopus subject areas

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Cite this