A New Method of Combined Classifier Design Based on Fuzzy Integral and Support Vector Machines
Kexin Jia, Youxin Lu · 2007
To make the modulation classification system more suitable for signals in a wide range of signal noise rate (SNR), a novel method of designing combined classifier based on fuzzy integral and multi-class support vector machines (MSVM) is presented in this paper. The method employs multi-class support vector machines classifiers and fuzzy integral to improve recognition reliability. Experimental results illustrate that the proposed combined classifier has high recognition rate with large variation range of SNR (success rates are over 98.2% when SNR is not lower than 5 dB).