Ant colony optimization with null heuristic factor for feature selection

Il Seok Oh; Jin Seon Lee

doi:10.1109/TENCON.2009.5395862

Ant colony optimization with null heuristic factor for feature selection

Il Seok Oh^*
, Jin Seon Lee

^*Corresponding author for this work

Woosuk University

Research output: Contribution to conference › Conference paper › peer-review

3 Scopus citations

Abstract

Recently, the ant colony optimization (ACO) metaheuristic has received more attention as an efficient searching method for feature selection. This paper addresses various solution representation schemes of ACO and their effectiveness with respect to whether they consider correlations between features. A generic code of ACO using on-edge representation is presented. The paper formulates the η-component by concentrating on the types of objects that participate in calculating the η value. Four schemes based on the formulation are compared in terms of the timing efficiency and accuracy. The experimental results showed that the null-η scheme is comparable to other schemes. We discuss the explanation of these conclusions.

Original language	English
Title of host publication	TENCON 2009 - 2009 IEEE Region 10 Conference
DOIs	https://doi.org/10.1109/TENCON.2009.5395862
State	Published - 2009
Event	2009 IEEE Region 10 Conference, TENCON 2009 - Singapore, Singapore Duration: 2009.11.23 → 2009.11.26

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON

Conference

Conference	2009 IEEE Region 10 Conference, TENCON 2009
Country/Territory	Singapore
City	Singapore
Period	09.11.23 → 09.11.26

Keywords

Ant colony optimization
Feature selection
Heuristic factor
Pattern recogntion

Quacquarelli Symonds(QS) Subject Topics

Computer Science & Information Systems
Engineering - Electrical & Electronic
Engineering - Petroleum
Data Science

Access to Document

10.1109/TENCON.2009.5395862

Cite this

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title = "Ant colony optimization with null heuristic factor for feature selection",

abstract = "Recently, the ant colony optimization (ACO) metaheuristic has received more attention as an efficient searching method for feature selection. This paper addresses various solution representation schemes of ACO and their effectiveness with respect to whether they consider correlations between features. A generic code of ACO using on-edge representation is presented. The paper formulates the η-component by concentrating on the types of objects that participate in calculating the η value. Four schemes based on the formulation are compared in terms of the timing efficiency and accuracy. The experimental results showed that the null-η scheme is comparable to other schemes. We discuss the explanation of these conclusions.",

keywords = "Ant colony optimization, Feature selection, Heuristic factor, Pattern recogntion",

author = "Oh, \{Il Seok\} and Lee, \{Jin Seon\}",

year = "2009",

doi = "10.1109/TENCON.2009.5395862",

language = "English",

isbn = "9781424445479",

series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

booktitle = "TENCON 2009 - 2009 IEEE Region 10 Conference",

note = "2009 IEEE Region 10 Conference, TENCON 2009 ; Conference date: 23-11-2009 Through 26-11-2009",

}

TY - GEN

T1 - Ant colony optimization with null heuristic factor for feature selection

AU - Oh, Il Seok

AU - Lee, Jin Seon

PY - 2009

Y1 - 2009

N2 - Recently, the ant colony optimization (ACO) metaheuristic has received more attention as an efficient searching method for feature selection. This paper addresses various solution representation schemes of ACO and their effectiveness with respect to whether they consider correlations between features. A generic code of ACO using on-edge representation is presented. The paper formulates the η-component by concentrating on the types of objects that participate in calculating the η value. Four schemes based on the formulation are compared in terms of the timing efficiency and accuracy. The experimental results showed that the null-η scheme is comparable to other schemes. We discuss the explanation of these conclusions.

AB - Recently, the ant colony optimization (ACO) metaheuristic has received more attention as an efficient searching method for feature selection. This paper addresses various solution representation schemes of ACO and their effectiveness with respect to whether they consider correlations between features. A generic code of ACO using on-edge representation is presented. The paper formulates the η-component by concentrating on the types of objects that participate in calculating the η value. Four schemes based on the formulation are compared in terms of the timing efficiency and accuracy. The experimental results showed that the null-η scheme is comparable to other schemes. We discuss the explanation of these conclusions.

KW - Ant colony optimization

KW - Feature selection

KW - Heuristic factor

KW - Pattern recogntion

UR - https://www.scopus.com/pages/publications/77951122519

U2 - 10.1109/TENCON.2009.5395862

DO - 10.1109/TENCON.2009.5395862

M3 - Conference paper

AN - SCOPUS:77951122519

SN - 9781424445479

T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON

BT - TENCON 2009 - 2009 IEEE Region 10 Conference

T2 - 2009 IEEE Region 10 Conference, TENCON 2009

Y2 - 23 November 2009 through 26 November 2009

ER -

Ant colony optimization with null heuristic factor for feature selection

Abstract

Publication series

Conference

Keywords

Quacquarelli Symonds(QS) Subject Topics

Access to Document

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