misvm_orova {mildsvm}

Fit MI-SVM model to ordinal outcome data using One-vs-All

Description

This function uses the one-vs-all multiclass classification strategy to fit a series of MI-SVM models for predictions on ordinal outcome data. For an ordinal outcome with K levels, we fit K MI-SVM models to predict an individual level vs not.

Usage

## Default S3 method:
misvm_orova(
  x,
  y,
  bags,
  cost = 1,
  method = c("heuristic", "mip", "qp-heuristic"),
  weights = TRUE,
  control = list(kernel = "linear", sigma = if (is.vector(x)) 1 else 1/ncol(x),
    nystrom_args = list(m = nrow(x), r = nrow(x), sampling = "random"), max_step = 500,
    type = "C-classification", scale = TRUE, verbose = FALSE, time_limit = 60, start =
    FALSE),
  ...
)

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

## S3 method for class 'mi_df'
misvm_orova(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, bags⁠ are automatically extracted, 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.

bags

A vector specifying which instance belongs to each bag. Can be a string, numeric, of factor.

cost

The cost parameter in SVM. If method = 'heuristic', this will be fed to kernlab::ksvm(), otherwise it is similarly in internal functions.

method

The algorithm to use in fitting (default 'heuristic'). When method = 'heuristic', which employs an algorithm similar to Andrews et al. (2003). When method = 'mip', the novel MIP method will be used. When ⁠method = 'qp-heuristic⁠, the heuristic algorithm is computed using the dual SVM. See details.

weights

named vector, or TRUE, to control the weight of the cost parameter for each possible y value. Weights multiply against the cost vector. If TRUE, weights are calculated based on inverse counts of instances with given label, where we only count one positive instance per bag. Otherwise, names must match the levels of y.

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.

• nystrom_args a list of parameters to pass to kfm_nystrom(). This is used when method = 'mip' and kernel = 'radial' to generate a Nystrom approximation of the kernel features.

• max_step argument used when method = 'heuristic'. Maximum steps of iteration for the heuristic algorithm.

• type: argument used when method = 'heuristic'. The type argument is passed to e1071::svm().

• 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.

• time_limit argument used when method = 'mip'. FALSE, or a time limit (in seconds) passed to gurobi() parameters. If FALSE, no time limit is given.

• start argument used when method = 'mip'. If TRUE, the mip program will be warm_started with the solution from method = 'qp-heuristic' to potentially improve speed.

...

Arguments passed to or from other methods.

formula

a formula with specification mi(y, bags) ~ x which uses the mi function to create the bag-instance structure. This argument is an alternative to the ⁠x, y, bags⁠ 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 misvm_orova The object contains at least the following components:

• fits: a list of misvm objects with length equal to the number of classes in y. See misvm() for details on the misvm object.

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

• features: The names of features used in training.

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

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.

Author(s)

Sean Kent

References

Andrews, S., Tsochantaridis, I., & Hofmann, T. (2002). Support vector machines for multiple-instance learning. Advances in neural information processing systems, 15.

See Also

predict.misvm_orova() for prediction on new data.

Examples

data("ordmvnorm")
x <- ordmvnorm[, 3:7]
y <- ordmvnorm$bag_label
bags <- ordmvnorm$bag_name

mdl1 <- misvm_orova(x, y, bags)
predict(mdl1, x, new_bags = bags)