On 1/1/19 4:40 AM, Priyanka Purkayastha wrote:
I have a dataset (data) with 700 rows and 7000 columns. I am trying to do
recursive feature selection with the SVM model. A quick google search
helped me get a code for a recursive search with SVM. However, I am unable
to understand the first part of the code, How do I introduce my dataset in
the code?
Generally the "labels" is given to such a machine learning device as the
y argument, while the "features" are passed as a matrix to the x argument.
--
David.
If the dataset is a matrix, named data. Please give me an example for
recursive feature selection with SVM. Bellow is the code I got for
recursive feature search.
svmrfeFeatureRanking = function(x,y){
#Checking for the variables
stopifnot(!is.null(x) == TRUE, !is.null(y) == TRUE)
n = ncol(x)
survivingFeaturesIndexes = seq_len(n)
featureRankedList = vector(length=n)
rankedFeatureIndex = n
while(length(survivingFeaturesIndexes)>0){
#train the support vector machine
svmModel = svm(x[, survivingFeaturesIndexes], y, cost = 10,
cachesize=500,
scale=FALSE, type="C-classification", kernel="linear" )
#compute the weight vector
w = t(svmModel$coefs)%*%svmModel$SV
#compute ranking criteria
rankingCriteria = w * w
#rank the features
ranking = sort(rankingCriteria, index.return = TRUE)$ix
#update feature ranked list
featureRankedList[rankedFeatureIndex] =
survivingFeaturesIndexes[ranking[1]]
rankedFeatureIndex = rankedFeatureIndex - 1
#eliminate the feature with smallest ranking criterion
(survivingFeaturesIndexes = survivingFeaturesIndexes[-ranking[1]])}
return (featureRankedList)}
I tried taking an idea from the above code and incorporate the idea in my
code as shown below
library(e1071)
library(caret)
data<- read.csv("matrix.csv", header = TRUE)
x <- data
y <- as.factor(data$Class)
svmrfeFeatureRanking = function(x,y){
#Checking for the variables
stopifnot(!is.null(x) == TRUE, !is.null(y) == TRUE)
n = ncol(x)
survivingFeaturesIndexes = seq_len(n)
featureRankedList = vector(length=n)
rankedFeatureIndex = n
while(length(survivingFeaturesIndexes)>0){
#train the support vector machine
svmModel = svm(x[, survivingFeaturesIndexes], y, cross=10,cost =
10, type="C-classification", kernel="linear" )
#compute the weight vector
w = t(svmModel$coefs)%*%svmModel$SV
#compute ranking criteria
rankingCriteria = w * w
#rank the features
ranking = sort(rankingCriteria, index.return = TRUE)$ix
#update feature ranked list
featureRankedList[rankedFeatureIndex] =
survivingFeaturesIndexes[ranking[1]]
rankedFeatureIndex = rankedFeatureIndex - 1
#eliminate the feature with smallest ranking criterion
(survivingFeaturesIndexes = survivingFeaturesIndexes[-ranking[1]])}
return (featureRankedList)}
But couldn't do anything at the stage "update feature ranked list"
Please guide
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and provide commented, minimal, self-contained, reproducible code.
