A Review of Ensemble Classification for DNA Microarrays Data

Ensemble classification has been a frequent topic of research in recent years, especially in bioinformatics. The benefits of ensemble classification (less prone to overfitting, increased classification performance, and reduced bias) are a perfect match for a number of issues that plague bioinformati...

Full description

Saved in:
Bibliographic Details
Published in2013 IEEE 25th International Conference on Tools with Artificial Intelligence pp. 381 - 389
Main Authors Khoshgoftaar, Taghi M., Dittman, David J., Wald, Randall, Awada, Wael
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.11.2013
Subjects
Artificial intelligence
Bagging
Bias
Bioinformatics
Bioinformtics
Boosting
Classification
Conferences
Decision trees
Deoxyribonucleic acid
DNA
DNA Microarray
Ensemble Learning
Expert systems
Genes
Neural networks
Noise
Vegetation
Online AccessGet full text

Cover

More Information
Summary:Ensemble classification has been a frequent topic of research in recent years, especially in bioinformatics. The benefits of ensemble classification (less prone to overfitting, increased classification performance, and reduced bias) are a perfect match for a number of issues that plague bioinformatics experiments. This is especially true for DNA microarray data experiments, due to the large amount of data (results from potentially tens of thousands of gene probes per sample) and large levels of noise inherent in the data. This work is a review of the current state of research regarding the applications of ensemble classification for DNA microarrays. We discuss what research thus far has demonstrated, as well as identify the areas where more research is required.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Conference-1
ObjectType-Feature-3
content type line 23
SourceType-Conference Papers & Proceedings-2
ISSN:1082-3409
DOI:10.1109/ICTAI.2013.64