Title page for etd-0210103-112153


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URN etd-0210103-112153
Author Fun-Zhu Lai
Author's Email Address laifz@ibm7.math.nsysu.edu.tw
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Department Applied Mathematics
Year 2002
Semester 1
Degree Master
Type of Document
Language English
Title GA-Based fuzzy clustering applied to irregular
Date of Defense 2003-01-08
Page Count 55
Keyword
  • Fuzzy Clustering
  • Genetic Algorithm
  • Simulated Annealing
  • Abstract   Building a rule-based classification system for a training data set is an important research topic in the area of data mining, knowledge discovery and expert systems. Recently, the GA-based fuzzy approach is shown to be an effective way to design an efficient evolutionary fuzzy system. In this thesis a three layers genetic algorithm with Simulated Annealing for selecting a small number of fuzzy if-then rules to building a compact fuzzy classification system will be proposed.
      The rule selection problem with three objectives: (1) maximize the number of correctly classified patterns, (2) minimize the number of fuzzy if-then rules, and (3) minimize the number of required features. Genetic algorithms are applied to solve this problem. A set of fuzzy if-then rules is coded into a binary string and treated as an in-dividual in genetic algorithms. The fitness of each individual is specified by three ob-jectives in the combinatorial optimization problem. Simulated annealing (SA) is op-tionally cooperated with three layers genetic algorithm to effectively select some layer control genes.
      The performance of the proposed method for training data and test data is ex-amined by computer simulations on the iris data set and spiral data set, and comparing the performance with the existing approaches. It is shown empirically that the pro-posed method outperforms the existing methods in the design of optimal fuzzy sys-tems.
    Advisory Committee
  • Pau-Choo Chung - chair
  • Zi-Cai Li - co-chair
  • Ching-Fen Jiang - co-chair
  • Chii-Maw Uang - advisor
  • Files
  • etd-0210103-112153.pdf
  • indicate access worldwide
    Date of Submission 2003-02-10

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