A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data
Serdica Journal of Computing (2014)
- Volume: 8, Issue: 2, page 137-168
- ISSN: 1312-6555
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topBill, Jo, and Fokoue, Ernest. "A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data." Serdica Journal of Computing 8.2 (2014): 137-168. <http://eudml.org/doc/269897>.
@article{Bill2014,
abstract = {This research evaluates pattern recognition techniques on a subclass of big data
where the dimensionality of the input space (p) is much larger than the number of
observations (n). Specifically, we evaluate massive gene expression microarray cancer data
where the ratio κ is less than one. We explore the statistical and computational challenges
inherent in these high dimensional low sample size (HDLSS) problems and present
statistical machine learning methods used to tackle and circumvent these difficulties.
Regularization and kernel algorithms were explored in this research using seven datasets
where κ < 1. These techniques require special attention to tuning necessitating
several extensions of cross-validation to be investigated to support better predictive
performance. While no single algorithm was universally the best predictor, the regularization
technique produced lower test errors in five of the seven datasets studied.},
author = {Bill, Jo, Fokoue, Ernest},
journal = {Serdica Journal of Computing},
keywords = {HDLSS; Machine Learning Algorithm; Pattern Recognition; Classification; Prediction; Regularization; Discriminant Analysis; Support Vector Machine; Kernels; Cross Validation; Microarray Cancer Data},
language = {eng},
number = {2},
pages = {137-168},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data},
url = {http://eudml.org/doc/269897},
volume = {8},
year = {2014},
}
TY - JOUR
AU - Bill, Jo
AU - Fokoue, Ernest
TI - A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data
JO - Serdica Journal of Computing
PY - 2014
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 8
IS - 2
SP - 137
EP - 168
AB - This research evaluates pattern recognition techniques on a subclass of big data
where the dimensionality of the input space (p) is much larger than the number of
observations (n). Specifically, we evaluate massive gene expression microarray cancer data
where the ratio κ is less than one. We explore the statistical and computational challenges
inherent in these high dimensional low sample size (HDLSS) problems and present
statistical machine learning methods used to tackle and circumvent these difficulties.
Regularization and kernel algorithms were explored in this research using seven datasets
where κ < 1. These techniques require special attention to tuning necessitating
several extensions of cross-validation to be investigated to support better predictive
performance. While no single algorithm was universally the best predictor, the regularization
technique produced lower test errors in five of the seven datasets studied.
LA - eng
KW - HDLSS; Machine Learning Algorithm; Pattern Recognition; Classification; Prediction; Regularization; Discriminant Analysis; Support Vector Machine; Kernels; Cross Validation; Microarray Cancer Data
UR - http://eudml.org/doc/269897
ER -
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