@inproceedings{31af37d163cd42ebbeb79a6e903c8c93,
title = "A heuristic method for selecting support features from large datasets",
abstract = "For feature selection in machine learning, set covering (SC) is most suited, for it selects support features for data under analysis based on the individual and the collective roles of the candidate features. However, the SC-based feature selection requires the complete pair-wise comparisons of the members of the different classes in a dataset, and this renders the meritorious SC principle impracticable for selecting support features from a large number of data. Introducing the notion of implicit SC-based feature selection, this paper presents a feature selection procedure that is equivalent to the standard SC-based feature selection procedure in supervised learning but with the memory requirement that is multiple orders of magnitude less than the counterpart. With experiments on six large machine learning datasets, we demonstrate the usefulness of the proposed implicit SCbased feature selection scheme in large-scale supervised data analysis.",
keywords = "Combinatorial optimization, Feature selection, Large datasets, Supervised learning",
author = "Ryoo, {Hong Seo} and Jang, {In Yong}",
year = "2007",
doi = "10.1007/978-3-540-72870-2_39",
language = "English",
isbn = "9783540728689",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "411--423",
booktitle = "Algorithmic Aspects in Information and Management - Third International Conference, AAIM 2007, Proceedings",
note = "3rd International Conference on Algorithmic Aspects in Information and Management, AAIM 2007 ; Conference date: 06-06-2007 Through 08-06-2007",
}