@article{37dcc419f9b24fc3a92130758a4f68f4,
title = "Information-theoretic algorithm for feature selection",
abstract = "Feature selection is used to improve the efficiency of learning algorithms by finding an optimal subset of features. However, most feature selection techniques can handle only certain types of data. Additional limitations of existing methods include intensive computational requirements and inability to identify redundant variables. In this paper, we present a novel, information-theoretic algorithm for feature selection, which finds an optimal set of attributes by removing both irrelevant and redundant features. The algorithm has a polynomial computational complexity and is applicable to datasets of a mixed nature. The method performance is evaluated on several benchmark datasets by using a standard classifier (C4.5).",
keywords = "Classification, Feature selection, Information theory, Information-theoretic network",
author = "Mark Last and Abraham Kandel and Oded Maimon",
note = "Funding Information: This work was partially supported by the USF Center for Software Testing under grant no. 2108-004-00.",
year = "2001",
month = may,
doi = "10.1016/S0167-8655(01)00019-8",
language = "אנגלית",
volume = "22",
pages = "799--811",
journal = "Pattern Recognition Letters",
issn = "0167-8655",
publisher = "Elsevier B.V.",
number = "6-7",
}