AAAI2019.pdf
AAAI 会议特征选择算法，非平衡数据
节选段落一：
We has open-sourced AFS and the source code can be
found at https://github.com/upup123/AAAI-2019-AFS.
1 http://yann.lecun.com/exdb/mnist/
2 http://csc.lsu.edu/~saikat/n-mnist/
Experiment Settings
Datasets. The datasets used for experiments are summa-
rized in Table 1.节选段落二：
In: Proceedings of
the Thirty-Second AAAI Conference on Artificial Intelligence,
New Orleans, Louisiana, USA.
Gui, J., Z. Sun, S. Ji, D. Tao and T. Tan, 2017. Feature selection
based on structured sparsity: A comprehensive study.节选段落三：
In: AAAI. pp: 671-676.
Peng, H., F. Long and C. Ding, 2005. Feature selection based on
mutual information criteria of max-dependency, max-relevance,
and min-redundancy. IEEE Transactions on pattern analysis and
machine intelligence, 27(8): 1226-1238.
Robnik-Šikonja, M. and I. Kononenko, 2003.
We has open-sourced AFS and the source code can be
found at https://github.com/upup123/AAAI-2019-AFS.
1 http://yann.lecun.com/exdb/mnist/
2 http://csc.lsu.edu/~saikat/n-mnist/
Experiment Settings
Datasets. The datasets used for experiments are summa-
rized in Table 1.节选段落二：
In: Proceedings of
the Thirty-Second AAAI Conference on Artificial Intelligence,
New Orleans, Louisiana, USA.
Gui, J., Z. Sun, S. Ji, D. Tao and T. Tan, 2017. Feature selection
based on structured sparsity: A comprehensive study.节选段落三：
In: AAAI. pp: 671-676.
Peng, H., F. Long and C. Ding, 2005. Feature selection based on
mutual information criteria of max-dependency, max-relevance,
and min-redundancy. IEEE Transactions on pattern analysis and
machine intelligence, 27(8): 1226-1238.
Robnik-Šikonja, M. and I. Kononenko, 2003.
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