@inproceedings{35b56be8631f4807a1627d86b9667155,
title = "A SA-based feature selection and parameter optimization approach for support vector machine",
abstract = "Support Vector Machine (SVM) is a new technique for pattern classification, and is used in many applications. Kernel parameter setting in the SVM training process, along with feature selection, will significantly impact the classification accuracy. The objective of this study is to simultaneously optimize parameters while finding a subset of features without degrading SVM classification accuracy. A simulated annealing (SA) approach for feature selection and parameters optimization was developed. Several UCI datasets are tested using the SA-based approach and the grid algorithm which is a traditional method of performing parameter searching. Compared with the grid algorithm, the proposed SA-based approach significantly improves the classification accuracy rate and requires fewer input features for the SVM.",
author = "Lin, {S. W.} and Tseng, {T. Y.} and Chen, {S. C.} and Huang, {J. F.}",
year = "2007",
doi = "10.1109/ICSMC.2006.384599",
language = "英语",
isbn = "1424401003",
series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",
pages = "3144",
booktitle = "2006 IEEE International Conference on Systems, Man and Cybernetics",
note = "2006 IEEE International Conference on Systems, Man and Cybernetics ; Conference date: 08-10-2006 Through 11-10-2006",
}