Unity is Strength: Improving biomedical classification performance based on ensemble learning approaches

Emily Chia-Yu Su; Usman Iqbal; Yu Chuan (Jack) Li

doi:10.1016/S0169-2607(17)30256-0

Unity is Strength: Improving biomedical classification performance based on ensemble learning approaches

Emily Chia-Yu Su
, Usman Iqbal
, Yu Chuan (Jack) Li^*

^*Corresponding author for this work

Research output: Contribution to journal › Editorial › Research

1 Citation (Scopus)

Abstract

Supervised learning algorithms have been utilized by applications of artificial intelligence in many fields including biomedicine. The editors’ choice articles in this month all incorporated advanced ensemble learning to improve performance in biomedical classification problems.

Original language	English
Pages (from-to)	A1-A1
Number of pages	1
Journal	Computer Methods and Programs in Biomedicine
Volume	142
DOIs	https://doi.org/10.1016/S0169-2607(17)30256-0
Publication status	Published - 1 Apr 2017
Externally published	Yes

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title = "Unity is Strength: Improving biomedical classification performance based on ensemble learning approaches",

abstract = "Supervised learning algorithms have been utilized by applications of artificial intelligence in many fields including biomedicine. The editors{\textquoteright} choice articles in this month all incorporated advanced ensemble learning to improve performance in biomedical classification problems.",

author = "\{Chia-Yu Su\}, Emily and Usman Iqbal and \{(Jack) Li\}, \{Yu Chuan\}",

year = "2017",

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T1 - Unity is Strength: Improving biomedical classification performance based on ensemble learning approaches

AU - Chia-Yu Su, Emily

AU - Iqbal, Usman

AU - (Jack) Li, Yu Chuan

PY - 2017/4/1

Y1 - 2017/4/1

N2 - Supervised learning algorithms have been utilized by applications of artificial intelligence in many fields including biomedicine. The editors’ choice articles in this month all incorporated advanced ensemble learning to improve performance in biomedical classification problems.

AB - Supervised learning algorithms have been utilized by applications of artificial intelligence in many fields including biomedicine. The editors’ choice articles in this month all incorporated advanced ensemble learning to improve performance in biomedical classification problems.

UR - https://www.scopus.com/pages/publications/85015390763

U2 - 10.1016/S0169-2607(17)30256-0

DO - 10.1016/S0169-2607(17)30256-0

M3 - Editorial

C2 - 28325452

AN - SCOPUS:85015390763

SN - 0169-2607

VL - 142

SP - A1-A1

JO - Computer Methods and Programs in Biomedicine

JF - Computer Methods and Programs in Biomedicine

ER -

Unity is Strength: Improving biomedical classification performance based on ensemble learning approaches

Abstract

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