On the Greediness of Feature Selection Algorithms

Kan Deng and Andrew Moore
International Conference of Machine Learning (ICML '98), July, 1998.


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Abstract
Based on our analysis and experiments using real-world datasets, we find that the greediness of forward feature selection algorithms does not severely corrupt the accuracy of function approximation using the selected input features, but improves the efficiency significantly. Hence, we propose three greedier algorithms in order to further enhance the efficiency of the feature selection processing. We provide empirical results for linear regression, locally weighted regression and k-nearest-neighbor models. We also propose to use these algorithms to develop an offline Chinese and Japanese handwriting recognition system with auto matically configured, local models.

Notes

Text Reference
Kan Deng and Andrew Moore, "On the Greediness of Feature Selection Algorithms," International Conference of Machine Learning (ICML '98), July, 1998.

BibTeX Reference
@inproceedings{Deng_1998_2820,
   author = "Kan Deng and Andrew Moore",
   title = "On the Greediness of Feature Selection Algorithms",
   booktitle = "International Conference of Machine Learning (ICML '98)",
   month = "July",
   year = "1998",
}