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

ÖZBAKIR, LALE; Delice, YILMAZ

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

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ÖZBAKIR L., Delice Y.

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, vol.24, no.3, pp.491-500, 2011 (SCI-Expanded)

Publication Type: Article / Article
Volume: 24 Issue: 3
Publication Date: 2011
Doi Number: 10.1016/j.engappai.2010.11.008
Journal Name: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
Page Numbers: pp.491-500
Keywords: Artificial neural networks, Particle swarm optimization, Rule extraction, Data mining, Classification, EXTRACTING RULES, ROUGH SETS, ALGORITHM
Kayseri University Affiliated: No

Abstract

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.