@inproceedings{19ffc3368cba4c848047f7618f695ee5,
title = "Anytime algorithm for feature selection",
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 be stopped at any time and provide a partial subset of selected features. The quality of results is monitored by a new measure: fuzzy information gain. The algorithm performance is evaluated on several benchmark datasets.",
keywords = "Anytime algorithms, Feature selection, Fuzzy information gain, Information-theoretic network",
author = "Mark Last and Abraham Kandel and Oded Maimon and Eugene Eberbach",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2001.; 2nd International Conference on Rough Sets and Current Trends in Computing, RSCTC 2000 ; Conference date: 16-10-2000 Through 19-10-2000",
year = "2001",
month = jan,
day = "1",
doi = "10.1007/3-540-45554-X_66",
language = "English",
isbn = "3540430741",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "532--539",
editor = "Wojciech Ziarko and Yiyu Yao",
booktitle = "Rough Sets and Current Trends in Computing - 2nd International Conference, RSCTC 2000, Revised Papers",
address = "Germany",
}