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

fYear

2005

fDate

25-25 May 2005

Firstpage

420

Lastpage

423

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on

Conference_Location

Reno, NV

Print_ISBN

0-7803-9159-4

Type

conf

DOI

10.1109/FUZZY.2005.1452430

Filename

1452430

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3400151