svor_exc: Fit SVOR-EXC model to ordinal outcome data

In mildsvm: Multiple-Instance Learning with Support Vector Machines

Fit SVOR-EXC model to ordinal outcome data

Description

This function fits the Support Vector Ordinal Regression with Explicit Constraints based on the research of Chu and Keerthi (2007).

Usage

## Default S3 method:
svor_exc(
  x,
  y,
  cost = 1,
  method = c("smo"),
  weights = NULL,
  control = list(kernel = "linear", sigma = if (is.vector(x)) 1 else 1/ncol(x),
    max_step = 500, scale = TRUE, verbose = FALSE),
  ...
)

## S3 method for class 'formula'
svor_exc(formula, data, ...)

## S3 method for class 'mi_df'
svor_exc(x, ...)

Arguments

x	A data.frame, matrix, or similar object of covariates, where each row represents an instance. If a mi_df object is passed, y is automatically extracted, bags is ignored, and all other columns will be used as predictors.
y	A numeric, character, or factor vector of bag labels for each instance. Must satisfy length(y) == nrow(x). Suggest that one of the levels is 1, '1', or TRUE, which becomes the positive class; otherwise, a positive class is chosen and a message will be supplied.
cost	The cost parameter in SVM.
method	The algorithm to use in fitting (default 'smo'). When method = 'smo', the modified SMO algorithm from Chu and Keerthi (2007) is used.
weights	NULL, since weights are not implemented for this function.
control	list of additional parameters passed to the method that control computation with the following components: kernel either a character the describes the kernel ('linear' or 'radial') or a kernel matrix at the instance level. sigma argument needed for radial basis kernel. max_step argument used when method = 'heuristic'. Maximum steps of iteration for the heuristic algorithm. scale argument used for all methods. A logical for whether to rescale the input before fitting. verbose argument used when method = 'mip'. Whether to message output to the console.
...	Arguments passed to or from other methods.
formula	A formula with specification y ~ x. This argument is an alternative to the x, y arguments, but requires the data argument. See examples.
data	If formula is provided, a data.frame or similar from which formula elements will be extracted.

Value

An object of class svor_exc The object contains at least the following components:

• smo_fit: A fit object from running the modified ordinal smo algorithm.

• call_type: A character indicating which method svor_exc() was called with.

• features: The names of features used in training.

• levels: The levels of y that are recorded for future prediction.

• cost: The cost parameter from function inputs.

• n_step: The total steps used in the heuristic algorithm.

• x_scale: If scale = TRUE, the scaling parameters for new predictions.

Methods (by class)

• default: Method for data.frame-like objects

• formula: Method for passing formula

• mi_df: Method for mi_df objects, automatically handling bag names, labels, and all covariates. Use the bag_label as y at the instance level, then perform svor_exc() ignoring the MIL structure and bags.

Author(s)

Sean Kent

References

Chu, W., & Keerthi, S. S. (2007). Support vector ordinal regression. Neural computation, 19(3), 792-815. doi: 10.1162/neco.2007.19.3.792

See Also

predict.svor_exc() for prediction on new data.

Examples

data("ordmvnorm")
x <- ordmvnorm[, 3:7]
y <- attr(ordmvnorm, "instance_label")

mdl1 <- svor_exc(x, y)
predict(mdl1, x)

mildsvm documentation built on July 14, 2022, 9:08 a.m.