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Tune Angle-Based Classifiers by ET-Lasso

Source: R/et.abclass.R
et.abclass.Rd

Tune the regularization parameter for an angle-based large-margin classifier by the ET-Lasso method (Yang, et al., 2019).

Usage

et.abclass(
  x,
  y,
  intercept = TRUE,
  weight = NULL,
  loss = c("logistic", "boost", "hinge-boost", "lum"),
  control = list(),
  nstages = 2,
  refit = list(lambda = 1e-06),
  ...
)

Arguments

x

A numeric matrix representing the design matrix. No missing valus are allowed. The coefficient estimates for constant columns will be zero. Thus, one should set the argument intercept to TRUE to include an intercept term instead of adding an all-one column to x.

y

An integer vector, a character vector, or a factor vector representing the response label.

intercept

A logical value indicating if an intercept should be considered in the model. The default value is TRUE and the intercept is excluded from regularization.

weight

A numeric vector for nonnegative observation weights. Equal observation weights are used by default.

loss

A character value specifying the loss function. The available options are "logistic" for the logistic deviance loss, "boost" for the exponential loss approximating Boosting machines, "hinge-boost" for hybrid of SVM and AdaBoost machine, and "lum" for largin-margin unified machines (LUM). See Liu, et al. (2011) for details.

control

A list of control parameters. See abclass.control() for details.

nstages

A positive integer specifying for the number of stages in the ET-Lasso procedure. By default, two rounds of tuning by random permutations will be performed as suggested in Yang, et al. (2019).

refit

A logical value indicating if a new classifier should be trained using the selected predictors. This argument can also be a list with named elements, which will be passed to abclass.control() to specify how the new classifier should be trained.

...

Other control parameters passed to abclass.control().

References

Yang, S., Wen, J., Zhan, X., & Kifer, D. (2019). ET-Lasso: A new efficient tuning of lasso-type regularization for high-dimensional data. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 607--616).