GTbasedIM (version 1.0.0)

IM_dep: IM_dep Function: Calculate Influence Measure with Dependent Features

Description

The IM_dep function calculates the influence measure of equation (2) in Davila-Pena et al. (2024). The features are grouped into unions based on the index vector.

Usage

IM_dep(Xdata, Ydata, index)

Value

A vector of influences for each feature.

Arguments

Xdata

Matrix. A dataset where rows represent observations and columns represent features.

Ydata

Vector. The response variable associated with each row in Xdata.

index

Vector. A grouping vector that assigns each feature in Xdata to a specific union. E.g., if we have the partition set P={{1},{2,4},{3}}, then index=c(1,2,3,2).

Details

The IM_dep function calculates the weighted average of the number of times a change in the value of a feature associated to a specific union influences the response value.

References

Davila-Pena, L., Saavedra-Nieves, A., & Casas-Méndez, B. (2024). On the influence of dependent features in classification problems: a game-theoretic perspective. arXiv preprint. tools:::Rd_expr_doi("10.48550/arXiv.2408.02481").

Examples

# Example usage from Example 5.2 in Davila-Pena et al. (2024):

library(CoopGame)
n.user <- 16
Xdata <- createBitMatrix(4)[,-5]
Xdata <- rbind(c(0,0,0,0),Xdata)
Ydata <- rep(0,n.user)
Ydata[1+c(10,11,13,14,15)] <- 1

# Scenario 1:
IM_dep(Xdata,Ydata,index = c(1,2,3,4))
# Scenario 9:
IM_dep(Xdata,Ydata,index = c(1,2,1,2))