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.
| Identifer | oai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0210103-112153 |
| Date | 10 February 2003 |
| Creators | Lai, Fun-Zhu |
| Contributors | Ching-Fen Jiang, Chii-Maw Uang, Pau-Choo Chung, Zi-Cai Li |
| Publisher | NSYSU |
| Source Sets | NSYSU Electronic Thesis and Dissertation Archive |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0210103-112153 |
| Rights | unrestricted, Copyright information available at source archive |
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