Biocomb (version 0.4)

select.relief: Ranks the features

Description

This function calculates the features weights basing on a distance between instances. It can handle only numeric. The results is in the form of “data.frame”, consisting of the following fields: features (Biomarker) names, weights and the positions of the features in the dataset. The features in the data.frame are sorted according to the weight values. This function is used internally to perform the classification with feature selection using the function “classifier.loop” with argument “Chi-square” for feature selection. The variable “NumberFeature” of the data.frame is passed to the classification function.

Usage

select.relief(matrix)

Arguments

matrix

a dataset, a matrix of feature values for several cases, the last column is for the class labels. Class labels could be numerical or character values. The maximal number of classes is ten.

Value

The data can be provided with reasonable number of missing values that must be at first preprocessed with one of the imputing methods in the function input_miss. A returned data.frame consists of the the following fields:

Biomarker

a character vector of feature names

Weights

a numeric vector of weight values for the features

NumberFeature

a numerical vector of the positions of the features in the dataset

Details

This function's main job is to rank the features according to weights. See the “Value” section to this page for more details.

Data can be provided in matrix form, where the rows correspond to cases with feature values and class label. The columns contain the values of individual features and the last column must contain class labels. The maximal number of class labels equals 10. The class label features and all the nominal features must be defined as factors.

References

Y. Wang, I.V. Tetko, M.A. Hall, E. Frank, A. Facius, K.F.X. Mayer, and H.W. Mewes, "Gene Selection from Microarray Data for Cancer Classification<U+2014>A Machine Learning Approach," Computational Biology and Chemistry, vol. 29, no. 1, pp. 37-46, 2005.

See Also

input_miss, select.process

Examples

# NOT RUN {
# example for dataset without missing values
data(data_test)

# class label must be factor
data_test[,ncol(data_test)]<-as.factor(data_test[,ncol(data_test)])

out=select.relief(data_test)

# example for dataset with missing values
# }
# NOT RUN {
data(leukemia_miss)
xdata=leukemia_miss

# class label must be factor
xdata[,ncol(xdata)]<-as.factor(xdata[,ncol(xdata)])

# nominal features must be factors
attrs.nominal=101
xdata[,attrs.nominal]<-as.factor(xdata[,attrs.nominal])

delThre=0.2
out=input_miss(xdata,"mean.value",attrs.nominal,delThre)
if(out$flag.miss)
{
 xdata=out$data
}
out=select.relief(xdata)
# }