具有有效特徵選取及歸屬函數最佳化機制之模糊分類器之設計及應用

Design and Applications of Fuzzy Classifier with Effective Feature Selection and Membership Function Optimization

陳瑄易(Shyuan-Yi Chen)

指導教授 ： 洪欽銘

國立臺灣師範大學/科技學院/工業教育學系/碩士(2006年)

摘要

無資料

關鍵字

類神經-模糊網路 ； 分類 ； 特徵選取 ； K-means演算法 ； 歸屬函數

並列摘要

An efficient and simple decision support system must have the characteristics such as interpretable, easy understanding, convenient, et al. For this reason, the designed classifier in this study was based on a neuro-fuzzy network to combine the transparent characteristic of fuzzy system and learning ability of neural network. First, this study proposes a refined K-means algorithm and a gradient-based learning algorithm to logically determine and adaptively tuned the fuzzy membership functions for the employed neuro-fuzzy network. Moreover, this study also uses grey relational algorithm to perform feature selection and proposes a novel feature reduction algorithm to overcome the drawbacks of grey relational algorithm.
Because optimized processes contain complex and long steps, this study proposes a Fast Graph Fuzzy Classifier (FGFC) which has a novel determining scheme of the membership function degree and can prevent to confront an abstruse classifier algorithm as well as keep the advantages of the traditional fuzzy systems. All of the above-mentioned methods were implemented and analyzed in this study.

並列關鍵字

Neuro-Fuzzy Network Classifier ； Classification ； Feature Selection ； K-means Algorithm ； Membership Function

參考文獻

[8] J. Shanahan, B.T. Thomas, M. Mirmehdi, M. Martin, N. Campbell, J. Baldwin, “A soft computing approach to road classification,” J. Intell. Robot. Systems vol.29, pp.349–387. 2000

[1] R. Lee and J. Liu, “iJADE WeatherMAN: a weather forecasting system using intelligent multiagent-based fuzzy neuro network,” IEEE Trans. Sys. Man. Cyber., vol. 34, no. 3, pp.369-377, 2004

[2] H. Li and D. Enke, “Forecasting series-based stock price data using direct reinforcement learning,” Proc. 2004 Int. Conf. neural networks, vol. 2, pp.1103-21108, 2004

[3] C. Fredembach, M. Schroder, and S. Susstrunk, “Eigenregions for image classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 12, pp.1645-1649, 2004

[4] P. R. Innocent and R. I. John, “Computer aided fuzzy medical diagnosis,” Artif. Intell. Med., vol. 162, pp.81-104, 2004.