Finding feature transformation functions using genetic algorithm

Eun Yeong Ahn; Tracy Mullen; John Yen

doi:10.1145/1830761.1830862

Finding feature transformation functions using genetic algorithm

Eun Yeong Ahn, Tracy Mullen, John Yen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication
Pages	2061-2062
Number of pages	2
DOIs	https://doi.org/10.1145/1830761.1830862
State	Published - 2010
Event	12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010 - Portland, OR, United States Duration: Jul 7 2010 → Jul 11 2010

Other

Other	12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010
Country/Territory	United States
City	Portland, OR
Period	7/7/10 → 7/11/10

All Science Journal Classification (ASJC) codes

Computational Theory and Mathematics
Theoretical Computer Science

Access to Document

10.1145/1830761.1830862

Cite this

@inproceedings{199b04ecdbb14ade97955183a74cc76c,

title = "Finding feature transformation functions using genetic algorithm",

abstract = "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.",

author = "Ahn, {Eun Yeong} and Tracy Mullen and John Yen",

year = "2010",

doi = "10.1145/1830761.1830862",

language = "English (US)",

isbn = "9781450300735",

pages = "2061--2062",

booktitle = "Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication",

note = "12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010 ; Conference date: 07-07-2010 Through 11-07-2010",

}

Ahn, EY, Mullen, T & Yen, J 2010, Finding feature transformation functions using genetic algorithm. in Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 - Companion Publication. pp. 2061-2062, 12th Annual Genetic and Evolutionary Computation Conference, GECCO-2010, Portland, OR, United States, 7/7/10. https://doi.org/10.1145/1830761.1830862

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

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 -

Finding feature transformation functions using genetic algorithm

Abstract

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this