Exploring comprehensible classification rules from trained neural networks integrated with a time-varying binary particle swarm optimizer

ÖZBAKIR, LALE; Delice, Yılmaz

doi:10.1016/j.engappai.2010.11.008

Exploring comprehensible classification rules from trained neural networks integrated with a time-varying binary particle swarm optimizer

ÖZBAKIR L., Delice Y.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, cilt.24, sa.3, ss.491-500, 2011 (SCI-Expanded, Scopus)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 24 Sayı: 3
Basım Tarihi: 2011
Doi Numarası: 10.1016/j.engappai.2010.11.008
Dergi Adı: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Sayfa Sayıları: ss.491-500
Anahtar Kelimeler: Artificial neural networks, Particle swarm optimization, Rule extraction, Data mining, Classification
Erciyes Üniversitesi Adresli: Evet

Özet

Purpose: Extracting comprehensible classification rules is the most emphasized concept in data mining researches. In order to obtain accurate and comprehensible classification rules from databases, a new approach is proposed by combining advantages of artificial neural networks (ANN) and swarm intelligence.