A hybrid ensemble method based on double disturbance for classifying microarray data.
Biomed Mater Eng
; 26 Suppl 1: S1961-8, 2015.
Article
em En
| MEDLINE
| ID: mdl-26405970
ABSTRACT
Microarray data has small samples and high dimension, and it contains a significant amount of irrelevant and redundant genes. This paper proposes a hybrid ensemble method based on double disturbance to improve classification performance. Firstly, original genes are ranked through reliefF algorithm and part of the genes are selected from the original genes set, and then a new training set is generated from the original training set according to the previously selected genes. Secondly, D bootstrap training subsets are produced from the previously generated training set by bootstrap technology. Thirdly, an attribute reduction method based on neighborhood mutual information with a different radius is used to reduce genes on each bootstrap training subset to produce new training subsets. Each new training subset is applied to train a base classifier. Finally, a part of the base classifiers are selected based on the teaching-learning-based optimization to build an ensemble by weighted voting. Experimental results on six benchmark cancer microarray datasets showed proposed method decreased ensemble size and obtained higher classification performance compared with Bagging, AdaBoost, and Random Forest.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Modelos Estatísticos
/
Análise de Sequência com Séries de Oligonucleotídeos
/
Perfilação da Expressão Gênica
/
Aprendizado de Máquina
/
Proteínas de Neoplasias
/
Neoplasias
Tipo de estudo:
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Biomed Mater Eng
Assunto da revista:
BIOTECNOLOGIA
/
ENGENHARIA BIOMEDICA
Ano de publicação:
2015
Tipo de documento:
Article
País de afiliação:
China