DocumentCode :
3227625
Title :
A Review of Ensemble Classification for DNA Microarrays Data
Author :
Khoshgoftaar, Taghi M. ; Dittman, David J. ; Wald, Randall ; Awada, Wael
Author_Institution :
Florida Atlantic Univ., Boca Raton, FL, USA
fYear :
2013
fDate :
4-6 Nov. 2013
Firstpage :
381
Lastpage :
389
Abstract :
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.
Keywords :
DNA; bioinformatics; learning (artificial intelligence); pattern classification; DNA microarrays data; bioinformatics; ensemble classification; Bagging; Bioinformatics; Boosting; DNA; Decision trees; Neural networks; Vegetation; Bioinformtics; DNA Microarray; Ensemble Learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on
Conference_Location :
Herndon, VA
ISSN :
1082-3409
Print_ISBN :
978-1-4799-2971-9
Type :
conf
DOI :
10.1109/ICTAI.2013.64
Filename :
6735275
Link To Document :
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