Diagnosis System Based on Multiple Neural Classifiers

• Autorzy: Marciniak A. , Korbicz J.
• Języki publikacji: EN

Abstrakty

EN

A new methodology for improving the performance and training of neural network classifiers employed in diagnostics is presented. The main idea is based on using redundant classifiers in an ensemble in order to guarantee the best generalisation ability of the diagnosis system. A brief survey of some commonly used methods for combining outputs in the ensemble is made. As compared to previous designs, a novel method for output combination is introduced. The proposed technique consist in considering the classes independently of one another and calculating the importance parameters, i.e. the weights, for individual outputs of the networks. In order to draw a comparison with previous methods, a real data medical benchmark is used. To improve the results of the ensemble, Negative Correlation Learning was applied.

Słowa kluczowe

EN

classifiers; diagnosis; learning algorithms; neural networks; pattern recognition

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2001
• Tom: vol. 49, nr 4
• Strony: 681--701
• Opis fizyczny: bibliogr. 29 poz., rys., tab.

Twórcy

autor

Marciniak A.

autor

Korbicz J.

• Institute of Control and Computation Engineering, Technical University of Zielona Góra, 65-246 Zielona Góra, ul. Podgórna 50, Poland (Instytut Sterowabnia i Systemów Informatycznych)