A Comprehensive Analysis Of Classification Algorithms For Cancer Prediction From Gene Expression
Document Type
Conference Proceeding
Publication Date
2015
Published In
Proceedings Of The 6th ACM Conference On Bioinformatics, Computational Biology And Health Informatics
Abstract
With the advent of inexpensive microarray technology, biologists have become increasingly reliant on gene expression analysis for detecting disease states, including diagnosis of cancerous tissue [12]. While random forests and SVMs have proven to be popular methods for expression analysis, little work has been done to compare these methods with AdaBoost, a popular ensemble learning algorithm, across a wide array of cancer prediction tasks. Our work shows AdaBoost outperforms other approaches on binary predictions while random forests and SVMs are the best choice in multi-class predictions.
Published By
ACM
Conference
6th ACM Conference On Bioinformatics, Computational Biology And Health Informatics
Conference Dates
September 9-12, 2015
Conference Location
Atlanta, GA
Recommended Citation
Raehoon Jeong , '17 and Ameet Soni.
(2015).
"A Comprehensive Analysis Of Classification Algorithms For Cancer Prediction From Gene Expression".
Proceedings Of The 6th ACM Conference On Bioinformatics, Computational Biology And Health Informatics.
525-526.
DOI: 10.1145/2808719.2811443
https://works.swarthmore.edu/fac-comp-sci/44