Feature subset selection method for AdaBoost training

San Yuan Zhao; Ting Zhi Shen; Chen Sheng Sun; Peng Zhang Liu; Lei Yue

Feature subset selection method for AdaBoost training

San Yuan Zhao^*, Ting Zhi Shen, Chen Sheng Sun, Peng Zhang Liu, Lei Yue

^*Corresponding author for this work

School of Computer Science and Technology

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

The feature-selection problem in training AdaBoost classifiers is addressed in this paper. A working feature subset is generated by adopting a novel feature subset selection method based on the partial least square (PLS) regression, and then trained and selected from this feature subset in Boosting. The experiments show that the proposed PLS-based feature-selection method outperforms the current feature ranking method and the random sampling method.

Original language	English
Pages (from-to)	399-402
Number of pages	4
Journal	Journal of Beijing Institute of Technology (English Edition)
Volume	20
Issue number	3
Publication status	Published - Sept 2011

Keywords

Boosting method
Dimensionality reduction
Feature subset

Cite this

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title = "Feature subset selection method for AdaBoost training",

abstract = "The feature-selection problem in training AdaBoost classifiers is addressed in this paper. A working feature subset is generated by adopting a novel feature subset selection method based on the partial least square (PLS) regression, and then trained and selected from this feature subset in Boosting. The experiments show that the proposed PLS-based feature-selection method outperforms the current feature ranking method and the random sampling method.",

keywords = "Boosting method, Dimensionality reduction, Feature subset",

author = "Zhao, {San Yuan} and Shen, {Ting Zhi} and Sun, {Chen Sheng} and Liu, {Peng Zhang} and Lei Yue",

year = "2011",

month = sep,

language = "English",

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journal = "Journal of Beijing Institute of Technology (English Edition)",

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T1 - Feature subset selection method for AdaBoost training

AU - Zhao, San Yuan

AU - Shen, Ting Zhi

AU - Sun, Chen Sheng

AU - Liu, Peng Zhang

AU - Yue, Lei

PY - 2011/9

Y1 - 2011/9

N2 - The feature-selection problem in training AdaBoost classifiers is addressed in this paper. A working feature subset is generated by adopting a novel feature subset selection method based on the partial least square (PLS) regression, and then trained and selected from this feature subset in Boosting. The experiments show that the proposed PLS-based feature-selection method outperforms the current feature ranking method and the random sampling method.

AB - The feature-selection problem in training AdaBoost classifiers is addressed in this paper. A working feature subset is generated by adopting a novel feature subset selection method based on the partial least square (PLS) regression, and then trained and selected from this feature subset in Boosting. The experiments show that the proposed PLS-based feature-selection method outperforms the current feature ranking method and the random sampling method.

KW - Boosting method

KW - Dimensionality reduction

KW - Feature subset

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M3 - Article

AN - SCOPUS:80054837490

SN - 1004-0579

VL - 20

SP - 399

EP - 402

JO - Journal of Beijing Institute of Technology (English Edition)

JF - Journal of Beijing Institute of Technology (English Edition)

IS - 3

ER -

Feature subset selection method for AdaBoost training

Abstract

Keywords

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