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An Improved SMO Algorithm for Credit Risk Evaluation
Wang, J., Lu, A. and Jiang, X.
Sequential minimal optimization (SMO) is the most
commonly used algorithm for numerical solution of
SVM, but traditional SMO is quite limited to the
long execution time because of its high computational
complexity. We present an improved SMO learning
algorithm named FV-SMO in this paper. At each iteration, it jointly optimizes four variables and an theorem is proposed to guarantee an analytical solution
of sub-problem. Three credit datasets are selected
to demonstrate the performance of FV-SMO, including China credit dataset and two public datasets:
German credit dataset from UCI and Darden credit
dataset from CD-ROM databases. China credit
dataset is generated based on a multi-dimensional
and multi-level credit risk indicator system of China
credit data. Experimental results demonstrate that
FV-SMO is competitive in saving the computational
cost and performs best in credit risk evaluation accuracy compared with other five popular classification
methods. |
Cite as: Wang, J., Lu, A. and Jiang, X. (2015). An Improved SMO Algorithm for Credit Risk Evaluation. In Proc. Thirteenth Australasian Data Mining Conference (AusDM 2015) Sydney, Australia. CRPIT, 168. Ong, K.L., Zhao, Y., Stone, M.G. and Islam, M.Z. Eds., ACS. 169-176 |
(from crpit.com)
(local if available)
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