TY - GEN
T1 - Anytime algorithm for feature selection
AU - Last, Mark
AU - Kandel, Abraham
AU - Maimon, Oded
AU - Eberbach, Eugene
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2001.
PY - 2001
Y1 - 2001
N2 - 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.
AB - 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.
KW - Anytime algorithms
KW - Feature selection
KW - Fuzzy information gain
KW - Information-theoretic network
UR - http://www.scopus.com/inward/record.url?scp=84957803501&partnerID=8YFLogxK
U2 - 10.1007/3-540-45554-X_66
DO - 10.1007/3-540-45554-X_66
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AN - SCOPUS:84957803501
SN - 3540430741
SN - 9783540430742
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 532
EP - 539
BT - Rough Sets and Current Trends in Computing - 2nd International Conference, RSCTC 2000, Revised Papers
A2 - Ziarko, Wojciech
A2 - Yao, Yiyu
PB - Springer Verlag
T2 - 2nd International Conference on Rough Sets and Current Trends in Computing, RSCTC 2000
Y2 - 16 October 2000 through 19 October 2000
ER -