Title :
Support Vector Machine with the Fuzzy Hybrid Kernel for Protein Subcellular Localization Classification
Author :
Jin, Bo ; Tang, Yuchun ; Zhang, Yan-Qing ; Lu, Chung-Dar ; Weber, Irene
Author_Institution :
Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA
Abstract :
In the paper, we present a fuzzy hybrid kernel that combines several conventional kernels by using the TSK model. The major technical merit is to make a more reliable kernel fusing different kernels. Support vector machine (SVM) with the fuzzy hybrid kernel is employed for protein subcellular localization classification. Experimental results indicate that SVM with the new fuzzy hybrid kernel is better than those with conventional kernels
Keywords :
biology computing; cellular biophysics; fuzzy logic; fuzzy systems; pattern classification; proteins; scientific information systems; support vector machines; TSK model; bioinformatics; cellular biophysics; fuzzy hybrid kernel; fuzzy logic; fuzzy systems; protein subcellular localization classification; support vector machines; Bioinformatics; Fuzzy logic; Fuzzy systems; Kernel; Machine learning; Pattern recognition; Proteins; Support vector machine classification; Support vector machines; Training data;
Conference_Titel :
Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on
Conference_Location :
Reno, NV
Print_ISBN :
0-7803-9159-4
DOI :
10.1109/FUZZY.2005.1452430
