fsSimilarityMatrix: Constructing the similarity matrix

In fsMTS: Feature Selection for Multivariate Time Series

Constructing the similarity matrix

Description

fsSimilarityMatrix constructs a square matrix of similarity metric values between MTS feature sets. Metrics are calculated using fsSimilarity function with cutting-off feature sets

Usage

fsSimilarityMatrix(feature.sets, threshold, method)

Arguments

feature.sets	a list of matrixes that contains weights for features, estimated by several feature selection algorithms.
threshold	the required sparsity of the resulting feature set
method	a similarity metric. Directly passed to fsSimilarity function

Value

returns a real-valued square matrix with pairwise similarity metric values of feature sets

See Also

fsSimilarity

Examples

# Load traffic data
data(traffic.mini)

# Scaling is sometimes useful for feature selection
# Exclude the first column - it contains timestamps
data <- scale(traffic.mini$data[,-1])

mIndep<-fsMTS(data, max.lag=3, method="ownlags")
mCCF<-fsMTS(data, max.lag=3, method="CCF")
mDistance<-fsMTS(data, max.lag=3, method="distance", shortest = traffic.mini$shortest, step = 5)
mGLASSO<-fsMTS(data, max.lag=3,method="GLASSO", rho = 0.05)
mLARS<-fsMTS(data, max.lag=3,method="LARS")
mRF<-fsMTS(data, max.lag=3,method="RF")
mMI<-fsMTS(data, max.lag=3,method="MI")
mlist <- list(Independent = mIndep,
              Distance = mDistance,
              CCF = mCCF,
              GLASSO = mGLASSO,
              LARS = mLARS,
              RF = mRF,
              MI = mMI)

(msimilarity <- fsSimilarityMatrix(mlist,threshold = 0.3, method="Kuncheva"))

fsMTS documentation built on April 26, 2022, 9:05 a.m.