I am trying to find out the relationship among three features, so I
think it would be clearer if I can visualize the three features into
one 3D graph. Does anybody has any idea about this?
Thanks in advance,
Computing and Information Sciences
Kansas State University
I have issues to utilize conversion from string to word vector.
I have test and training data set which some string data only
existing in test dataset but not training data set. As it is
applied to conversion. It results in incompatible datasets.
Since the string value will be replaced with another attribute
in test data set arff file. Any pointer to resolve the issue
for model training and validation.
Thanks for your reply (below). But how do I change the CLASSPATH
environment variable without crashing my computer? I'm using Windows
XP and have tried to see about altering the CLASSPATH environment
variable by going into start > control panel > system > advanced (tab)
> environment variables. But it looks like CLASSPATH is a system
variable and I'm not sure what I am doing. Is this where I am
supposed to add "libsvm.jar"? And am I supposed to separate it from
what is already there with a semicolon? Do I risk killing my OS?
> I'm trying to use Weka version 3.5.5 to run LibSVM. I get the error
> message "libsvm not in CLASSPATH". How do I fix this?
libsvm is a 3rd-party tool and is not included in Weka (the LibSVM
classifier is wrapper, using Reflection to call the libsvm library).
Just download it from the libsvm hompage and then include the libsvm.jar
in your CLASSPATH environment variable.
More information about libsvm:
I did some classification tasks with WEKA 3.4 using the DecisionTable classifier, and it worked perfectly without any error. Now, I'm having problems on the evaluation phase using WEKA 3.5.6 with DecisionTable. I get this error message:
Problem evaluating classifier: weka.classifiers.Evaluation
What can I do?
I suppose it's related to the classifier settings... I tried to change them, but I get always the same error message, which was not present using WEKA 3.4
I used weka 3.5.6 to do regression on my data. I used SVMreg choosing
polyKernel, and got this:
weights (not support vectors):
- 0.3499 * (normalized) x
Can I just take this equation as the regression equation? I mean y= -
0.3499*(normailized)x + 0.5315 ? Would someone please tell me how the x is
normalized in weka?
Bioinformatics and Computational Biology program @ ISU
Ames, IA 50010
Thanks Thomas for the reply. I was reading the paper, Instance-based
learning algorithm by Aha and Kibler (1991), that Weka IB1 and IBk
classifiers implement. My impression was that in the paper, IB1, IB2, and
IB3 refer to three different instance-based algorithms. My guess was
that I could specify the number of neighbors, k, for each of these algorithms.
In other words, the number in the names IB1, IB2, and IB3 in the paper
does not seem to correspond with the number of neighbors I choose but
denote the three variations. So, which algorithm could the Weka IBk
implementation be? Maybe I should look at the code long enough to figure
> you can specify the number of Nearest Neighbours, which choice exactly
> makes you use IB1, IB2 etc.
> 2009/5/13 Li Yang <lyshane(a)umich.edu>
>> Dear Weka experts,
>> I was just wondering whether the IBk classifier implements the IB1, IB2, or
>> IB3 algorithm in Aha and Kibler's article, Instance-based learning algorithm
>> Thank you in advance for your help.
>> Wekalist mailing list
>> Send posts to: Wekalist(a)list.scms.waikato.ac.nz
>> List info and subscription status:
>> List etiquette:
> Departement of Knowledge Engeneering
> Faculty of Humanities & Science
> Maastricht University
I am using weka 3.6 API programatically for association rule mining using apriori. Essentially, I am doing market
basket analysis for an electronic store. I have 7 attributes as follows with values as either "Y" or "N", depending
on whether an item is present or not in a transaction.
An example instance is
"Y", "Y", "Y", "N","N", "N", "N"
My problem is that rules seem to be dominated by the negatively correlated attributes, which is understandable.
Here are the top 5 rules I got
1. hdtv=N tv_stand=N 9 ==> lap_top=Y 9 conf:(1)
2. lap_top=Y hdtv=N 9 ==> tv_stand=N 9 conf:(1)
3. anti_virus_software=Y connector_cable=N 8 ==> lap_top=Y 8 conf:(1)
4. anti_virus_software=Y tv_stand=N 8 ==> lap_top=Y 8 conf:(1)
5. hdtv=N connector_cable=N 8 ==> lap_top=Y 8 conf:(1)
I am more interested in rules based on positive correlation. In the result above, I am not interested in rules 1, 2 and 5.
How do I get rid of them. Will it help to replace "N" with missing value. How do I specify missing value when I create instances
I would appreciate any help
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I can see that weka.core.ContingencyTables class calculates various
statistics about a dataset (e.g. conditional entropy of row=feature given a
Is there a class that would 'implement' this class that would just output
these statistics without running any classifier algorithm (e.g. J48 that
uses conditional entropies to calculate information gain)?
I am a Java rookie so for any help big or little I would be grateful!
Thanks, Harri S
I'm reading the learned model into an
ObjectInputStream object and creating a Classifier
from it. Then I'm creating an Instance with a,b,c as
variables and setting y=0 which is the classifier
Then I'm running
clsLabel = aClassifier.classifyInstance(anInstance);
If I use LinearRegression, this is working fine. But,
if I use a MultilayerPerceptron classifier, I'm
getting an error:
Exception in thread "main"
weka.core.UnassignedDatasetException : Instance
doesn't have access to a dataset!
Do I need to do something different for
Can anyone tell me where I'm going wrong ?