Classifier.par: A function to perform classification task by multi-core...

In stepwiseCM: Stepwise Classification of Cancer Samples using High-dimensional Data Sets

Description

Classification has been embedded inside the parallel processing procedure to speed up the computation for large data set. Instead of performing sequential execution of the outer cross-validation (see below), function performs parallel execution.

Usage

Classifier.par(train, test = NULL, train.label, type = c("TSP", "GLM", "GLM_L1", 
               "GLM_L2", "PAM", "SVM", "plsrf_x", "plsrf_x_pv", "RF"), 
               CVtype = c("loocv", "k-fold"), outerkfold = 5, innerkfold = 5, 
               ncpus = 2)

Arguments

train	An object of class ExpressionSet or data frame or matrix contains predictors for the training set, where columns correspond to samples and rows to features.
test	An object of class ExpressionSet or data frame or matrix contains predictors for the test set (optional), where columns correspond to samples and rows to features.
train.label	A numeric vector contains the actual class labels (0 or 1) of the training set. NOTE: values should be numeric not factor.
type	Type of classification algorithm used. Currently 9 well-known algorithm are available for user to choose from. They are: top scoring pair (TSP), logistic regression (GLM), GLM with L1 (lasso) penalty, GLM with L2 (ridge) penalty, prediction analysis for microarray (PAM), support vector machine (SVM), Random Forest combined with partial least square dimension reduction (plsrf_x), Random Forest combined with partial least square dimension reduction plus pre-validation (plsrf_x_pv), Random Forest (RF). NOTE: "TSP", "PAM", "plsrf_x" and "plsrf_x_pv" are exclusively for high-dimensional data.
CVtype	Cross-validation type used to obtain the predicted labels of the training set. Must be either k-fold cross-validation (k-fold), or leave-one-out-cross-validation (loocv).
outerkfold	Number of cross validation used in the training phase.
innerkfold	Number of cross validation used to estimate the model parameters. E.g. penalty parameter in "GLM_L1".
ncpus	Number of cores assign to the parallel computation.

Value

A list object contains following components:

P.train	A numeric vector contains the predicted labels of the training set.
P.test	A numeric vector contains the predicted labels of the test set (if test set is given).

Author(s)

Askar Obulkasim

Maintainer: Askar Obulkasim <[e-mail]>

References

Aik Choo Tan and Daniel Q. Naiman and Lei Xu and Raimond L. Winslow and Donald Geman(2005). Simple Decision Rules for Classifying Human Cancers from Gene Expression Profiles(TSP). Bioinformatics, 21, 3896-3904.

Anne-Laure Boulesteix and Christine Porzelius and Martin Daumer(2008). Microarray-based Classification and Clinical Predictors: on Combined Classifiers and Additional Predictive Value. Bioinformatics, 24, 1698–1706.

Examples

data(CNS)
train <- CNS$mrna[, 1:40]
test <- CNS$mrna[, 41:60]
train.label <- CNS$class[1:40]
## Not run: Pred <- Classifier.par(train = train, test = test, train.label = train.label, type = "GLM_L1", 
                        CVtype = "k-fold", outerkfold = 5, innerkfold = 5, ncpus = 5)
## End(Not run)

stepwiseCM documentation built on May 31, 2017, 11:47 a.m.