Computes the Jaccard index using Gower's dissimilarity.
Usage
JaccardRate(
data,
data_red,
k=6
)
Value
Jaccard Index
numeric
Arguments
data
A data frame. Values of type 'numeric' or 'integer' are treated as numerical.
data_red
A data frame. A subset of data with the selected features.
k
number of neighbors
References
Zhao, Z., L. Wang, and H. Liu (2010). Efficient spectral feature selection with minimum redundancy.
In Proceedings of the AAAI conference on artificial intelligence, Volume 24, pp. 673–678.
data(ESI)
data=ESI[,-c(1,3,4,6,9)] ##removing categorical featuresout=UFS(data,alpha=0.01,method='c',pv_adj='BH')
JR=JaccardRate(data,out$selected.features)
JR #visualize the index