selectFFS {BioSeqClass} | R Documentation

## feature forward selection

### Description

feature forward selection.

### Usage

      selectFFS(data, accCutoff, stop.n,
                classifyMethod="knn",cv=10)

### Arguments

`data` | a data frame including the feature matrix and class label. The last column is a vector of class label comprising of "-1" or "+1"; Other columns are features.
`accCutoff` | a numeric indicating the minimum difference of accuracy between two models in `selectFFS`. Feature subsets will stop increasing when the difference of accuracy is samll than accCutoff.
`stop.n` | number of selected features by `selectFFS`.
`classifyMethod` | a string for the classification method. This must be one of the strings "libsvm", "svmlight", "NaiveBayes", "randomForest", "knn", "tree", "nnet", "rpart", "ctree", "ctreelibsvm", "bagging".
`cv` | an integer for the time of cross validation, or a string "leave\_one\_out" for the jacknife test.

### Details

`selectFFS` uses FFS (Feature Forword Selection) method to increase feature, and use NNA (Neareast Neighbor Analysis) to evaluate the performance of feature subset. Two conditions are used to stop feature increasing: control the difference of accuracy between two models; control the number of selected features by Parameter "stop.n".

### Author(s)

Hong Li

### Examples

      ## read positive/negative sequence from files.
      tmpfile1 = file.path(path.package("BioSeqClass"), "example", "acetylation_K.pos40.pep")
      tmpfile2 = file.path(path.package("BioSeqClass"), "example", "acetylation_K.neg40.pep")
      posSeq = as.matrix(read.csv(tmpfile1,header=FALSE,sep="\t",row.names=1))[,1]
      negSeq = as.matrix(read.csv(tmpfile2,header=FALSE,sep="\t",row.names=1))[,1]
      seq=c(posSeq,negSeq)
      classLable=c(rep("+1",length(posSeq)),rep("-1",length(negSeq)) )
      data = data.frame(featureBinary(seq),classLable)

      if(interactive()){
        ## Use KNN to evaluate the performance of feature subset,
        ## and use Feature Forword Selection method to increase feature.
        # If the difference of accuracy between two models is less than 0.01, feature
        # selection will stop.
        FFS_NNA_CV5 = selectFFS(data,accCutoff=0.01,classifyMethod="knn",cv=5)
        # If 20 features have been selected, feature selection will stop.
        FFS_NNA_CV5 = selectFFS(data,stop.n=3,classifyMethod="knn",cv=5)
        # If any one condiction is satisfied, feature selection will stop.
        FFS_NNA_CV5 = selectFFS(data,accCutoff=0.001,stop.n=100,classifyMethod="knn",cv=5)
      }

[Package BioSeqClass version 1.40.0 Index]