Logistic ensembles of Random Spherical Linear Oracles for microarray classification.
Random Spherical Linear Oracles (RSLO) for DNA microarray gene expression data are proposed for classifier fusion. RSLO employs random hyperplane splits of samples in the principal component score space based on the first three principal components (X, Y, Z) of the input feature set. Hyperplane splits are used to assign training(testing) samples to separate logistic regression mini-classifiers, which increases the diversity of voting results since errors are not shared across mini-classifiers. We recommend use of RSLO with 3-4 10-fold CV and re-partitioning samples randomly every ten iterations prior to each 10-fold CV. This equates to a total of 30-40 iterations.
Peterson
Leif E
LE
Center for Biostatistics, The Methodist Hospital Research Institute, Houston, TX 77030, USA. lepeterson@tmhs.org
Coleman
Matthew A
MA
D002965:Classification / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D016015:Logistic Models; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D025341:Principal Component Analysis