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Abstract: Feature selection is used to improve performance of learning algorithms by finding a minimal subset of relevant features. Since the process of feature selection is computationally intensive, a trade-off between the quality of the selected subset and the computation time is required. In this paper, we are presenting a novel, anytime algorithm for feature selection, which gradually improves the quality of results by increasing the computation time. The algorithm is interruptible, i.e., it can ... (Update)
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BibTeX entry: (Update)
@article{ last01anytime,
author = "Mark Last and Abraham Kandel and Oded Maimon and Eugene Eberbach",
title = "Anytime Algorithm for Feature Selection",
journal = "Lecture Notes in Computer Science",
volume = "2005",
pages = "532--??",
year = "2001",
url = "citeseer.ist.psu.edu/763776.html" }
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