Title :
Support Vector regression hybrid algorithm based on Rough Set
Author :
Deng, Jiuying ; Chen, Qiang ; Mao, Zongyuan ; Gao, Xiangjun
Author_Institution :
Dept. of Comput. Sci., Guangdong Inst. of Educ., Guangzhou
Abstract :
Support vector machine has good generality. Its development for function regressing is not as same as that with fast speed for sample separated. Sequence minimum optimizing (SMO) is effective on large samples, and is used to handle the problems with sparse solutions. Considering the power of rough set (RS) for handling imprecise data, the datum boundary sought by RS will substitute original inputs as training subset. As the size of both training set and support vectors gained reduce, learning machine can be promoted and favor high quality solutions. Based on rough set and SMO algorithm of regression, a hybrid algorithm (RS-SMO-RA) is presented for function regressing. Only a simple and short module is need to makeup for differentiating boundary sample, and then algorithm RS-SMO-RA can outperform common regression algorithm of SMO. At last, experimental results are displayed with two approaches. There are evaluations of two algorithms implementing and testing.
Keywords :
learning (artificial intelligence); optimisation; regression analysis; rough set theory; support vector machines; function regressing; learning machine; rough set theory; sequence minimum optimizing; support vector regression hybrid algorithm; Computer science; Computer science education; Educational institutions; Educational technology; Machine learning; Pattern recognition; Stochastic resonance; Support vector machine classification; Support vector machines; Testing; SMO algorithm; boundary sample; rough set; support vector regression;
Conference_Titel :
Cybernetics and Intelligent Systems, 2008 IEEE Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-1673-8
Electronic_ISBN :
978-1-4244-1674-5
DOI :
10.1109/ICCIS.2008.4670768