Fuzzy DIFACONN-miner: A novel approach for fuzzy rule extraction from neural networks


KULLUK S., ÖZBAKIR L., BAYKASOĞLU A.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.40, sa.3, ss.938-946, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 40 Sayı: 3
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1016/j.eswa.2012.05.050
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.938-946
  • Anahtar Kelimeler: Classification, Artificial neural networks, Fuzzy rule based systems, CLASSIFICATION
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Artificial neural networks (ANNs) are mathematical models inspired from the biological nervous system. They have the ability of predicting, learning from experiences and generalizing from previous examples. An important drawback of ANNs is their very limited explanation capability, mainly due to the fact that knowledge embedded within ANNs is distributed over the activations and the connection weights. Therefore, one of the main challenges in the recent decades is to extract classification rules from ANNs. This paper presents a novel approach to extract fuzzy classification rules (FCR) from ANNs because of the fact that fuzzy rules are more interpretable and cope better with pervasive uncertainty and vagueness with respect to crisp rules. A soft computing based algorithm is developed to generate fuzzy rules based on a data mining tool (DIFACONN-miner), which was recently developed by the authors. Fuzzy DIFA-CONN-miner algorithm can extract fuzzy classification rules from datasets containing both categorical and continuous attributes. Experimental research on the benchmark datasets and comparisons with other fuzzy rule based classification (FRBC) algorithms has shown that the proposed algorithm yields high classification accuracies and comprehensible rule sets. (C) 2012 Elsevier Ltd. All rights reserved.