TY - GEN
T1 - Finding feature transformation functions using genetic algorithm
AU - Ahn, Eun Yeong
AU - Mullen, Tracy
AU - Yen, John
PY - 2010
Y1 - 2010
N2 - Identifying a good set of features is critical to the performance of learning algorithms such as classifiers. Previous methods have focused on either selecting a subset of features or transforming features using principle components analysis. In this paper, we propose a genetic algorithm approach that searches for a good feature transformation function over a subset of features using a novel representation scheme with novel reproduction operators. Preliminary experimental results using the UCI data set show promising results.
AB - Identifying a good set of features is critical to the performance of learning algorithms such as classifiers. Previous methods have focused on either selecting a subset of features or transforming features using principle components analysis. In this paper, we propose a genetic algorithm approach that searches for a good feature transformation function over a subset of features using a novel representation scheme with novel reproduction operators. Preliminary experimental results using the UCI data set show promising results.
UR - http://www.scopus.com/inward/record.url?scp=77955973815&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77955973815&partnerID=8YFLogxK
U2 - 10.1145/1830761.1830862
DO - 10.1145/1830761.1830862
M3 - Conference contribution
AN - SCOPUS:77955973815
SN - 9781450300735
T3 - Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication
SP - 2061
EP - 2062
BT - Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication
T2 - 12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010
Y2 - 7 July 2010 through 11 July 2010
ER -