metaheur: metaheuristic optimization of preprocessing combinations

In metaheur: Metaheuristic Optimization Framework for Preprocessing Combinations

Description

metaheuristic optimization of preprocessing combinations

Usage

metaheur(gridclassobject, startgrid = 0, startnum = 1, iterations = 10,
  taboolistlength = 1, initialtemperature = 0.01, tempconst = 0.01,
  reheat = 0.01, nholdout = 2, late = 0, stopcond = 1,
  stopvalue = 0.99, deltafive = 0.05, model = "rpart", cores = 1)

Arguments

gridclassobject	(GridClass) created by setgrid function in preprocomb package
startgrid	(integer) 0 random restart (default), 1 grid restart
startnum	(integer) number of restarts
iterations	(integer) number of iterations done for a restart, defaults to 10
taboolistlength	(integer) number of previous solution that can not be revisited, must be 1 or more
initialtemperature	(numeric) initial propability for acccepting an inferior candidate, between 0 and 1
tempconst	(numeric) multiplier for decreasing temperature on each iteration
reheat	(numeric) propability of increasing temperature on each iteration
nholdout	(integer) number of holdout rounds, defaults to 2
late	(integer) location of previous best solution a candidate is compared to, defaults to 0 for last
stopcond	(integer) type of stopping condition in addition to iterations, default to 1 for threshold, 2 for convergence
stopvalue	(numeric) threshold for stopping, defaults to 0.99
deltafive	(numeric) convergence criteria for last five iterations, defaults to 0.05
model	(character) caret name of predictive model, defaults to "rpart"
cores	(integer) number of cores used in computation of classification accuracy holdout rounds, defaults to 1

Examples

## result <- metaheur(examplegrid, startnum=2, nholdout=2, cores=2)
## getbestheur(result)

metaheur documentation built on May 1, 2019, 10:50 p.m.