Package kernelfactory
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1 Package kernelfactory September 29, 2015 Type Package Title Kernel Factory: An Ensemble of Kernel Machines Version Date Imports randomforest, AUC, genalg, kernlab, stats Author Michel Ballings, Dirk Van den Poel Maintainer Michel Ballings Binary classification based on an ensemble of kernel machines (``Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), ''). Kernel factory is an ensemble method where each base classifier (random forest) is fit on the kernel matrix of a subset of the training data. License GPL (>= 2) NeedsCompilation no Repository CRAN Date/Publication :33:15 R topics documented: Credit kernelfactory kfnews predict.kernelfactory Index 7 1
2 2 kernelfactory Credit Credit approval (Frank and Asuncion, 2010) Credit contains credit card applications. The dataset has a good mix of continuous and categorical features. Format A data frame with 653 observations, 15 predictors and a binary criterion variable called Response Details All observations with missing values are deleted. Source Frank, A. and Asuncion, A. (2010). UCI Machine Learning Repository [ Irvine, CA: University of California, School of Information and Computer Science. The original dataset can be downloaded at str(credit) table(credit$response) kernelfactory Binary classification with Kernel Factory kernelfactory implements an ensemble method for kernel machines (Ballings and Van den Poel, 2013).
3 kernelfactory 3 kernelfactory(x = NULL, y = NULL, cp = 1, rp = round(log(nrow(x), 10)), method = "burn", ntree = 500, filter = 0.01, popsize = rp * cp * 7, iters = 80, mutationchance = 1/(rp * cp), elitism = max(1, round((rp * cp) * 0.05)), oversample = TRUE) Arguments x y cp rp Value method ntree filter popsize iters A data frame of predictors (numeric, integer or factor). Categorical variables need to be factors. Indicator values should not be too imbalanced because this might produce constants in the subsetting process. A factor containing the response vector. Only {0,1} is allowed. The number of column partitions. The number of row partitions. Can be one of the following: POLynomial kernel function (pol), LINear kernel function (lin), Radial Basis kernel Function rbf), random choice (random=pol, lin, rbf) (random), burn- in choice of best function (burn=pol, lin, rbf ) (burn). Use random or burn if you don t know in advance which kernel function is best. Number of trees in the Random Forest base classifiers. either NULL (deactivate) or a percentage denoting the minimum class size of dummy predictors. This parameter is used to remove near constants. For example if nrow(xtrain)=100, and filter=0.01 then all dummy predictors with any class size equal to 1 will be removed. Set this higher (e.g., 0.05 or 0.10) in case of errors. Population size of the genetic algorithm. Number of generations of the genetic algorithm. mutationchance Mutationchance of the genetic algorithm. elitism oversample Elitism parameter of the genetic algorithm. Oversample the smallest class. This helps avoid problems related to the subsetting procedure (e.g., if rp is too high). An object of class kernelfactory, which is a list with the following elements: trn trnlst rbfstre rbfmtrx rsltskf cpr rpr cntr Training data set. List of training partitions. List of used kernel functions. List of augmented kernel matrices. List of models. Number of column partitions. Number of row partitions. Number of partitions.
4 4 kfnews wghts nmdtrn rngs constants Weights of the ensemble members. Vector indicating the numeric (and integer) features. Ranges of numeric predictors. To exclude from newdata. Author(s) Authors: Michel Ballings and Dirk Van den Poel, Maintainer: Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), See Also predict.kernelfactory #Credit Approval data available at UCI Machine Learning Repository #take subset (for the purpose of a quick example) and train and test Credit <- Credit[1:100,] train.ind <- sample(nrow(credit),round(0.5*nrow(credit))) #Train Kernel Factory on training data kfmodel <- kernelfactory(x=credit[train.ind,names(credit)!= "Response"], y=credit[train.ind,"response"], method=random) #Deploy Kernel Factory to predict response for test data #predictedresponse <- predict(kfmodel, newdata=credit[-train.ind,names(credit)!= "Response"]) kfnews Display the NEWS file kfnews shows the NEWS file of the kernelfactory package. kfnews() Value None.
5 predict.kernelfactory 5 Author(s) Authors: Michel Ballings and Dirk Van den Poel, Maintainer: <Michel.Ballings@GMail.com> Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), See Also kernelfactory, predict.kernelfactory kfnews() predict.kernelfactory Predict method for kernelfactory objects Prediction of new data using kernelfactory. ## S3 method for class kernelfactory predict(object, newdata = NULL, predict.all = FALSE,...) Arguments Value object newdata predict.all An object of class kernelfactory, as created by the function kernelfactory A data frame with the same predictors as in the training data. TRUE or FALSE. If TRUE and rp and cp are 1 then the individual predictions of the random forest are returned. If TRUE and any of rp and cp or bigger than 1 then the predictions of all the members are returned.... Not used currently. A vector containing the response probabilities. Author(s) Authors: Michel Ballings and Dirk Van den Poel, Maintainer: <Michel.Ballings@GMail.com>
6 6 predict.kernelfactory Ballings, M. and Van den Poel, D. (2013), Kernel Factory: An Ensemble of Kernel Machines. Expert Systems With Applications, 40(8), See Also kernelfactory #Credit Approval data available at UCI Machine Learning Repository #take subset (for the purpose of a quick example) and train and test Credit <- Credit[1:100,] train.ind <- sample(nrow(credit),round(0.5*nrow(credit))) #Train Kernel Factory on training data kfmodel <- kernelfactory(x=credit[train.ind,names(credit)!= "Response"], y=credit[train.ind,"response"], method=random) #Deploy Kernel Factory to predict response for test data predictedresponse <- predict(kfmodel, newdata=credit[-train.ind,names(credit)!= "Response"])
7 Index Topic classification kernelfactory, 2 predict.kernelfactory, 5 Topic datasets Credit, 2 Credit, 2 kernelfactory, 2, 5, 6 kfnews, 4 predict.kernelfactory, 4, 5, 5 7
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