@inproceedings{4346cb7c936f4aa68da154f4dcaeb917,
title = "Data dimension reduction using rough sets for support vector classifier",
abstract = "This paper proposes an application of rough sets as a data preprocessing front end for support vector classifier (SVC). A novel multi-class support vector classification strategy based on binary tree is also presented. The binary tree extends the pairwise discrimination capability of the SVC to the multi-class case naturally. Experimental results on benchmark datasets show that proposed method can reduce computation complexity without decreasing classification accuracy compare to SVC without data preprocessing.",
keywords = "Dimension reduction, Rough sets, Support vector classifier",
author = "Genting Yan and Guangfu Ma and Liangkuan Zhu",
year = "2006",
doi = "10.1007/11795131\_67",
language = "英语",
isbn = "3540362975",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "462--467",
booktitle = "Rough Sets and Knowledge Technology - First International Conference, RSKT 2006, Proceedings",
address = "德国",
note = "First International Conference on Rough Sets and Knowledge Technology, RSKT 2006 ; Conference date: 24-07-2006 Through 26-07-2006",
}