Extraction of prototype-based threshold rules using neural training procedure

Marcin Blachnik; Mirosław Kordos; Włodzisław Duch

doi:10.1007/978-3-642-33266-1_32

Extraction of prototype-based threshold rules using neural training procedure

Marcin Blachnik
, Mirosław Kordos
, Włodzisław Duch

Katedra Informatyki Przemysłowej (RM/RM4)

University of Bielsko-Biala
Nicolaus Copernicus University in Toruń
Nanyang Technological University

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

1 Cytowanie z bazy Scopus

Abstrakt

Complex neural and machine learning algorithms usually lack comprehensibility. Combination of sequential covering with prototypes based on threshold neurons leads to a prototype-threshold based rule system. This kind of knowledge representation can be quite efficient, providing solutions to many classification problems with a single rule.

Język oryginału	angielski
Tytuł publikacji goszczącej	Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
Wydawca	Springer Verlag
Strony	255-262
Liczba stron	8
Edycja	PART 2
ISBN (drukowany)	9783642332654
Identyfikatory DOI	https://doi.org/10.1007/978-3-642-33266-1_32
Status publikacji	Opublikowano - 2012
Wydarzenie	22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne, Szwajcaria Czas trwania: 11 wrz 2012 → 14 wrz 2012

Seria publikacji

Nazwa	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Numer	PART 2
Tom	7553 LNCS
ISSN (drukowany)	0302-9743
ISSN (elektroniczny)	1611-3349

Konferencja

Konferencja	22nd International Conference on Artificial Neural Networks, ICANN 2012
Kraj/Terytorium	Szwajcaria
Miejscowość	Lausanne
Okres	11/09/12 → 14/09/12

Obszary tematyczne ASJC Scopus

Informatyka teoretyczna
Informatyka ogólna

Uzyskaj dostęp do dokumentu

10.1007/978-3-642-33266-1_32

Inne pliki i linki

Link do publikacji w Scopus

Cytowanie

Blachnik, M., Kordos, M., & Duch, W. (2012). Extraction of prototype-based threshold rules using neural training procedure. W Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings (PART 2 red., str. 255-262). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Tom 7553 LNCS, Nr PART 2). Springer Verlag. https://doi.org/10.1007/978-3-642-33266-1_32

Blachnik, Marcin ; Kordos, Mirosław ; Duch, Włodzisław. / Extraction of prototype-based threshold rules using neural training procedure. Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings. PART 2. red. Springer Verlag, 2012. str. 255-262 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{55ae9d60bfc249c0a9d24def5c3891eb,

title = "Extraction of prototype-based threshold rules using neural training procedure",

abstract = "Complex neural and machine learning algorithms usually lack comprehensibility. Combination of sequential covering with prototypes based on threshold neurons leads to a prototype-threshold based rule system. This kind of knowledge representation can be quite efficient, providing solutions to many classification problems with a single rule.",

keywords = "Data understanding, prototype-based rules, rule extraction",

author = "Marcin Blachnik and Miros{\l}aw Kordos and W{\l}odzis{\l}aw Duch",

year = "2012",

doi = "10.1007/978-3-642-33266-1\_32",

language = "English",

isbn = "9783642332654",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

number = "PART 2",

pages = "255--262",

booktitle = "Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings",

address = "Germany",

edition = "PART 2",

note = "22nd International Conference on Artificial Neural Networks, ICANN 2012 ; Conference date: 11-09-2012 Through 14-09-2012",

}

Blachnik, M, Kordos, M & Duch, W 2012, Extraction of prototype-based threshold rules using neural training procedure. w Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings. PART 2 wyd., Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), nr PART 2, tom 7553 LNCS, Springer Verlag, str. 255-262, 22nd International Conference on Artificial Neural Networks, ICANN 2012, Lausanne, Szwajcaria, 11/09/12. https://doi.org/10.1007/978-3-642-33266-1_32

Extraction of prototype-based threshold rules using neural training procedure. / Blachnik, Marcin; Kordos, Mirosław; Duch, Włodzisław.
Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings. PART 2. red. Springer Verlag, 2012. str. 255-262 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Tom 7553 LNCS, Nr PART 2).

Wyniki badań: Rozdział w książce/raport/materiał konferencyjny › Wkład w konferencję › recenzja

TY - GEN

T1 - Extraction of prototype-based threshold rules using neural training procedure

AU - Blachnik, Marcin

AU - Kordos, Mirosław

AU - Duch, Włodzisław

PY - 2012

Y1 - 2012

N2 - Complex neural and machine learning algorithms usually lack comprehensibility. Combination of sequential covering with prototypes based on threshold neurons leads to a prototype-threshold based rule system. This kind of knowledge representation can be quite efficient, providing solutions to many classification problems with a single rule.

AB - Complex neural and machine learning algorithms usually lack comprehensibility. Combination of sequential covering with prototypes based on threshold neurons leads to a prototype-threshold based rule system. This kind of knowledge representation can be quite efficient, providing solutions to many classification problems with a single rule.

KW - Data understanding

KW - prototype-based rules

KW - rule extraction

UR - https://www.scopus.com/pages/publications/84867666564

U2 - 10.1007/978-3-642-33266-1_32

DO - 10.1007/978-3-642-33266-1_32

M3 - Conference contribution

AN - SCOPUS:84867666564

SN - 9783642332654

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 255

EP - 262

BT - Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings

PB - Springer Verlag

T2 - 22nd International Conference on Artificial Neural Networks, ICANN 2012

Y2 - 11 September 2012 through 14 September 2012

ER -

Blachnik M, Kordos M, Duch W. Extraction of prototype-based threshold rules using neural training procedure. W Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings. PART 2 red. Springer Verlag. 2012. str. 255-262. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-33266-1_32

Extraction of prototype-based threshold rules using neural training procedure

Abstrakt

Seria publikacji

Konferencja

Obszary tematyczne ASJC Scopus

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Inne pliki i linki

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