similar to: Rotation Forest in R

Hello, All: What thoughts might you have on "The R Software Package Development Process"? I'm looking for ideas, materials, references, and / or collaborators for an article on this topic to be submitted to the Communications of the ACM. My limited experience with other languages such as Matlab and Python suggests that R may be fairly unique with CRAN and this

Dear All, I am finding trouble trying to guild a Wrapper using random forest to evaluate the subsets: I do: nombi <- make_Weka_filter("weka/filters/supervised/attribute/AttributeSelection") datbin<- nombi(gene ~., data=X1X2X4X5W, control =Weka_control( S=list("weka.attributeSelection.GeneticSearch"), E=list("weka.attributeSelection.WrapperSubsetEval"),B

Hello all, I am attempting to run an R script that makes use of RWeka. I am running SuSE Linux 9.3 with Java 1.5.0_06, R version 2.2.1, Weka 3-4-7, and Rweka 0.2-1. CLASSPATH="/usr/local/weka-3-4-7/weka.jar:/usr/local/JGR/JGR.jar" I receive the error: NewObject("weka/core/Instances","(Ljava/io/Reader;)V",...) failed Exception in thread "main"

Dear R-users, apologies for the total beginner's question, but I have been trying to solve this problem for ages and I seem to be getting nowhere. I also have tried to search through the archives of the R mailing list, but I am still left with my problem. How do I change the width of the bars for this simple barplot? I understand that the the "width" argument might do what I want

Dear All, I'd like to use Weka functions that are not implemented (do not have interface) in RWeka, like the Remove function and others in the future! The .java() functionality is for that purpose but I haven't seen any example with Weka functions. Could anyone give me hand in how to do it? For instace if I want to use the weka.filters.unsupervised.attribute.Remove? 1. in the R console,

Le jeudi 09 f?vrier 2012 ? 15:31 +0200, Kari Ruohonen a ?crit : > Hi, > I am trying to replicate a training of AttributeSelectedClassifier with > CFsSubsetEval, BestFirst and NaiveBayes that I have initially done with > Weka. Now, I am trying to use RWeka in R. > > I have a problem of passing arguments to the CfsSubsetEval, BestFirst > and NaiveBayes. I have first created an

Hello, I have some trouble in achieving the desired parametrisation for the weka classifier functions, using the package RWeka. The problem is, that the functions result=classifier(formula, data, subset, na.action, control = Weka_control(mycontrol)) do not seem to be manipulated by the mycontrol- arguments Perhaps this should be resepected via the handlers- argument , but the

I am familiar with the rpart and tree packages for classification and regression trees. However, quite a bit of the research in the transportation community relating to decision trees uses the C4.5 family of algorithms by Quinlan. Are there any plans to make a C4.5 (or a derivative of it) available to R? If not, then I might use the WEKA Java package ( http://www.cs.waikato.ac.nz/ml/weka) that

Hello, I have a trained Back Propagation Neural Network model in weka. I would like to re-evaluate the NN using R with a given input. How can I do this? I could not find an example of RWeca that applies to NN Thanks, Rui

Hello, I have two questions concerning the RWeka package: 1.) First question: How can one perform a cross validation, -say 10fold- for a given data set and given model ? 2.) Second question What is the correct syntax for the parametrization of e.g. Kernel classifiers interface m1 <- SMO(Species ~ ., data = iris, control =

Hello All, I am trying to carry out variable reduction. I do not have information about the dependent variable, and have only the X variables as it were. In selecting variables I wish to keep, I have considered the following criteria. 1) Percentage of missing value in each column/variable 2) Variance of each variable, with a cut-off value. I recently came across Weka and found that there is an

Hi, I want to save a RWeka model into a file, in order to retrive it latter with a load function. See this example: library(RWeka) NB <- make_Weka_classifier("weka/classifiers/bayes/NaiveBayes") model<-NB(formula,data=data,...) # does not run but you get the idea save(model,file="model.dat") # simple save R command # ... load("model.dat") # load the model

Hi everyone, I have trouble to use RWeka, I tried: (w=weather dataset, all preditors are nominal) > m<-J48(play~., data=w) > e<-evaluate_Weka_classifier(m,cost = matrix(c(0,2,1,0), + ncol = 2),numFolds = 10, complexity = TRUE,seed = 123, + class = TRUE) it gives me exactly what I want, but when I tried the same classifier on the other published data: (iris dataset has all numeric

Hi, I never use cobweb before and I'm quite new to this. I have a couple of questions around the cobweb implementation in R Weka. If you could supply answer or insight, I would really appreciate. 1. From Fisher's paper in 1987, it seems that Cobweb only deals with nominal data. In R Weka cobweb, is it allowed to accommodate real/continuous value? 2. My understanding is that Cobweb

> > After the basic profile-based transformations are working, I would like to > add new sources of profile. Mainly, I am thinking of implementing Auto > FDO. > For those who are not familiar with what autoFDO is -- Auto FDO is originally called Sample Based FDO. Its main author is Dehao Chen @google, and Robert Hundt is the one of the main pushers of technology in Google. The

Hi, guys: These days I keep using R and Weka to do data mining. I think my next step is open the source codes so that I can "customrize them" and make them better server my purpose. But now I kinda hesitate to do so b/c I am really not sure which is better to start with. You know, both require some time and I cannot clone myself to work on both:) If here are some persons who used both

Dear List, I´m trying to implement the functionalities from WEKA into my modeling project in R through the RWeka package. In this context I have a slightly special question about the filters implemented in WEKA. I want to convert nominal attributes with k values into k binary attributes through the NominalToBinary filter ("weka.filters.supervised.attribute.NominalToBinary"). But

Hi, I'm trying to use Information Gain for feature selection. There is a InfoGain implementation in Weka: *weka.attributeSelection.InfoGainAttributeEval* Is it possible to use this function with RWeka? If yes how? list_Weka_interfaces doesn't show it and there is no make function for AttributeEvaluators. Is there any other implementation of InformationGain in R? Thank you Michael

Hello, I have a question concerning decision trees coming from RWeka : library(RWeka) m =J48(Species~.,data=iris) How could such a decision tree be transferred into a matrix, pretty much in the same fashion, as it is done by getTree() in library(ofw) library(ofw) data(srbct) attach(srbct) ##ofwCART learn.cart.keep <- ofw(srbct,

Hi. I am not that good at R but I was wondering if there is either a tool or a strategy for testing many different models in R in a batch. I have used something in Weka called the Experimenter interface which helps with doing this kind of thing. Thank you. Andy [[alternative HTML version deleted]]