This function creates new DMUs to address data imbalances. If the majority class is efficient, it generates new inefficient DMUs by worsering the observed units. Conversely, if the majority class is inefficient, it projects inefficient DMUs to the frontier. Finally, a random selection if performed to keep a proportion of 0.65 for the majority class and 0.35 for the minority class.
get_SMOTE_DMUs(data, facets, x, y, RTS = "vrs", balance_data = NULL, seed)A list where each element corresponds to a balance level, containing a single data.frame
with the real and synthetic DMUs, correctly labeled.
A list of data.frames, where each element represents a dataset with labeled data.
A list where each element represents a subgroup containing index combinations that generate efficient units.
Column indexes of the input variables in the data.
Column indexes of the output variables in the data.
Text string or number defining the underlying DEA technology /
returns-to-scale assumption (default: "vrs"). Accepted values:
0 / "fdh"Free disposability hull, no convexity assumption.
1 / "vrs"Variable returns to scale, convexity and free disposability.
2 / "drs"Decreasing returns to scale, convexity, down-scaling and free disposability.
3 / "crs"Constant returns to scale, convexity and free disposability.
4 / "irs"Increasing returns to scale (up-scaling, not down-scaling), convexity and free disposability.
5 / "add"Additivity (scaling up and down, but only with integers), and free disposability.
A numeric vector indicating the different levels of balance required (e.g., c(0.1, 0.45, 0.6)).
Integer. Seed for reproducibility.