Support Vector regression hybrid algorithm based on Rough Set
Jiuying Deng, Qiang Chen, Zongyuan Mao, Xiangjun Gao · 2008
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.