Enhanced classification for high-throughput data with an optimal projection and hybrid classifier.
High-throughput screening technologies recently developed allow scientists to conduct millions of biological and medical tests simultaneously and rapidly. A major bottleneck for the analysis is to reduce the inherent high dimensionality for subsequent analysis. Principal Component Analysis (PCA) is a popular tool for dimensionality reduction by selecting typically a few Principal Components (PCs) ranked by their variances, eigenvalues. Since this selection approach is not always effective in reducing dimensionality, we consider a different ranking criterion, the canonical variate criterion. To further enhance the classification performance, we propose an integrated classification framework to combine the criterion and two hybrid classification methods and compare with several popular classification methods using leave-one-out cross-validation. For illustration, three real high-throughput data sets are considered and analysed to illustrate the methods.
Joon Jin
JJ
Song
Jingying
J
Zhang
D001185:Artificial Intelligence; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D025341:Principal Component Analysis