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Fuzzy rule extraction using hybrid evolutionary models for data mining systems
1157 viewed

Fuzzy rule extraction using hybrid evolutionary models for data mining systems

Edalat, I

Fuzzy rule extraction using hybrid evolutionary models for data mining systems

Edalat, I ; Sharif University of Technology | 2011

1157 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/AISP.2011.5960977
  3. Publisher: 2011
  4. Abstract:
  5. Data mining is a very popular technique which is successfully used in many areas. The aim of this paper is to present a Hybrid model for data classification from input datasets. The proposed model extracts knowledge using fuzzy rule based systems and performs classification task by fuzzy if-then rules. The proposed method performs the classification task and extracts required knowledge using fuzzy rule based systems which consists of fuzzy if-then rules. In order to do so the hybrid ant colony and simulated annealing algorithms have been used to optimize extracted fuzzy rule set. "ACSA", a self development data mining software system based on swarm intelligence, is applied to experiment on eight data sets taken from UCI Repository on Machine Learning. The results illuminate the algorithm proposed in this paper has better performance in classification accuracy. The results are compared with those of well-known methods, and show the systems competitive efficiency
  6. Keywords:
  7. Classification ; Fuzzy systems ; Ant colonies ; Ant colony algorithms ; Classification accuracy ; Classification tasks ; Data classification ; Data mining system ; Data sets ; Data-mining software ; Evolutionary models ; Fuzzy if-then rules ; Fuzzy rule extraction ; Fuzzy rule set ; Hybrid model ; On-machines ; Simulated annealing algorithms ; Swarm Intelligence ; UCI repository ; Algorithms ; Artificial intelligence ; Cellular automata ; Classification (of information) ; Fuzzy rules ; Signal processing ; Simulated annealing ; Data mining
  8. Source: 2011 International Symposium on Artificial Intelligence and Signal Processing, AISP 2011, 15 June 2011 through 16 June 2011 ; June , 2011 , Pages 25-30 ; 9781424498345 (ISBN)
  9. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=5960977