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
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;
Conference_Titel :
Tools with Artificial Intelligence (ICTAI), 2013 IEEE 25th International Conference on
Conference_Location :
Herndon, VA
Print_ISBN :
978-1-4799-2971-9
DOI :
10.1109/ICTAI.2013.64