Displaying 20 results from an estimated 700 matches similar to: "RBFNetwork in RWeka"
2007 Jul 11
2
RWeka control parameters classifiers interface
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
2007 Aug 01
1
RWeka cross-validation and Weka_control Parametrization
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 =
2012 Feb 09
1
Tr: Re: how to pass weka classifier options with a meta classifier in RWeka?
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
2012 Nov 12
0
Weka on command line c.f. using RWeka
Running Weka's command line with calls to system(), like this
> system("java weka.classifiers.bayes.NaiveBayes -K -t HWlrTrain.arff -o")
=== Confusion Matrix ===
a b <-- classified as
3518 597 | a = NoSpray
644 926 | b = Spray
=== Stratified cross-validation ===
=== Confusion Matrix ===
a b <-- classified as
3512 603 | a = NoSpray
2007 Nov 01
1
RWeka and naiveBayes
Hi
I'm trying to use RWeka to use a NaiveBayes Classifier(the Weka
version). However it crashes whenever there is a NA in the class
Gender
Here is the.code I have with d2 as the data frame.
The first call to NB doesn't make R crash but the second call does.
NB <- make_Weka_classifier("weka/classifiers/bayes/NaiveBayesSimple")
d2[,64]<-d2$Gender=="M"
2008 Oct 16
4
How to save/load RWeka models into/from a file?
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
2010 Aug 10
1
Why use numFold in evaluate_Weka_classifier of RWeka
Hi everyone,
I have a question about using RWeka package?
we know that instruction make_Weka_classifier that can help
us to build a model,and evaluate_Weka_classifier instruction
can help us to evaluate the performance of the model using on new data.
But I have a question about how to using the parameter numFold in
evaluate_Weka_classifier.Cross-validation means that using some parts
to
2012 Nov 08
0
FW: Interfacing R and Weka
-----Original Message-----
From: Patrick Connolly
Sent: Friday, 9 November 2012 11:29 a.m.
To: Peter Alspach
Subject: Interfacing R and Weka
> version
_
platform x86_64-unknown-linux-gnu
arch x86_64
os linux-gnu
system x86_64, linux-gnu
status
major 2
minor 15.2
year 2012
month 10
day 26
svn rev
2008 Oct 01
3
Installing RWeka package in CentOS 5: problems with JAVA?
Hi,
I am a R user, with some experience in MacOS, Linux, etc, but I am
having a problem that I cannot solve:
I have a linux server (CentOS 5) and I installed sun jdk1.6. For instance:
$ java -version
Java version "1.6.0_10-rc2"
Java(TM) SE Runtime Environment (build 1.6.0_10-rc2-b32)
Java HotSpot(TM) Server VM (build 11.0-b15, mixed mode)
I also installed the latest version of R:
2006 Mar 03
1
Java related (?) problems with RWeka
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"
2009 Jan 07
1
Question about the RWEKA package
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
2006 Nov 22
0
Problem with RWeka-rJava packages
Hello:
I´m trying to execute Apriori(file.arff) command of RWeka package.
I´m working with:
Operating System: Windows XP home
R-2.4.0
RWeka_0.2-11
rJava_0.4-11
classpath= .;C:\Archivos de programa\Java\jdk1.5.0\lib;C:\Archivos de
programa\R\R- 2.4.0\library\RWeka\jar
An error occurs when .jnew command is executed, on class
"weka/core/Instances" :
2009 Jun 04
1
About classifier in RWeka
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
2009 Dec 01
2
problem with RWeka Weka_control RandomForest
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
2009 Dec 02
0
RWeka problem with WrapperSubsetEval
Dear all,
I am trying to construct a wrapper that uses random forest to evaluate
the subsets using RWeka and when I do:
nombi <- make_Weka_filter("weka/attributeSelection/WrapperSubsetEval")
datbin<- nombi(gene ~., data=X1X2X3X4W, control =Weka_control( B =
list("weka.classifiers.trees.RandomForest")))
I also have tried with an other induction algorithm:
2009 Apr 23
2
RWeka: How to access AttributeEvaluators
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
2011 Mar 30
0
RWeka - adding weights to a dataset
Hello, I was wondering what would be the easiest way to append weights to a dataset in RWeka.
Ideally, I'd like to have something like:
m <- LogitBoost(Species ~ ., data=iris, weights = myweights)
But that, as far as I understand, it is not implemented and I'd need to use a workaround.
I know that when programming Weka in Java it is possible to assign weights to instances using
2011 Jun 04
1
Problem with Snowball & RWeka
I too have this problem. Everything worked fine last year, but after
updating R and packages I can no longer do word stemming.
Unfortunately, I didn't save the old binaries, otherwise I would just
revert back.
Hoping someone finds a solution for R on Windows. Thanks!
There is a potential solution for R on Mac OS from Kurt Hornik copied
below, but I cannot get this to work on Windows.
2006 Mar 05
2
RWeka
Hi,
I downloaded RWeka successfully. (At least I do not see, where I could have made a mistake.) Then I tried to load it by the library-command. To my surprise this did not work. Result:
library(RWeka)
Fehler in .jinit(c(system.file("jar", "weka.jar", package = "RWeka"), system.file("jar", :
Cannot create Java Virtual Machine
Fehler: .onLoad in
2008 Jun 17
1
Decision Trees RWeka
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,