I am samuel jebakumar doing My final Year MCA program in Karunya Univeristy
I am doing a project in weka tool , based on modification of Weka, i had
developed a new algorithm based on classifier.
To bring out novel approach in which, different methods are bisected to
generate a hybrid tree structure for tree based classification i.e. to
combine various methods like Gini index, Entropy, promoxities, information
gain, gain ratio etc.
I had added my algorithm in weka under classify tab under trees
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 am working on some dataset in which i have 5 different date variables in
it. the date variable in the dataset is in the format of YYYY-MM-dd (ISO
8601). I can insert the dataset in to WEKA-3-7-5. But the Problem is the
format of the date variable is in "Nominal". If i want to perform any
operations on the data or with the data, i need it in "Numeric" type.
The result that i got when i applied the "ChangeDateFormat" and the Weka is
not accepting the this format and giving me the message " Problem filtering
instances: Chosen attribute not date.".
Please help me in converting the NOMINAL to NUMERIC or with the solution.
Please Reply As soon As possible.
thank you very much.
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
Can someone please post few examples on HMM with sample dataset or if thr's
any link to a tutorial
On the web site by Marco Gillies, there is very little specifying how the
input arff/.csv file should be..
"The HMM classifiers only work on sequence data, which in Weka is
represented as a relational
Data instances must have a single, Nominal, class
attribute and a single, relational, sequence attribute. The instances in
this relational attibute may either consist of single, nominal data
instances (in the case of discrete HMMs) or multivariate, numeric attributes
(in the case of gaussian HMMs)."
I am not able to get exactly how should I go forward with this.
Please help me out here..
Well I'm calling SMOTE filter(WeKa) in my algorithm in MOA, it works fine
for some datasets but for some specific datasets it throws an exception
java.lang.IllegalArgumentException: Comparison method violates its general
at java.util.TimSort.mergeLo(Unknown Source)
at java.util.TimSort.mergeAt(Unknown Source)
at java.util.TimSort.mergeForceCollapse(Unknown Source)
at java.util.TimSort.sort(Unknown Source)
at java.util.TimSort.sort(Unknown Source)
at java.util.Arrays.sort(Unknown Source)
at java.util.Collections.sort(Unknown Source)
I went through some java forums which gave me pointers here
But i dont know how to get away with this problem...it will be great if
someone could help me out of this.
----- Abhijeet Godase.
The number of support vectors arenot available in Libsvm package in Weka. Please help me how can take number of support vectors for different types SVM as one-class svm, C-SVC and so on.
In the following code I initialize my classifier as K2 but I cant find how
to set estimator in the code using setScoreType(SelectedTag)
can you provide me the details to specify it.
public static void main(String args)
Instances train =
Instances test =
train.setClassIndex(train.numAttributes() - 1);
test.setClassIndex(test.numAttributes() - 1);
BayesNet myBayes = new BayesNet();
K2 myK2= new K2();
< myK2.setScoreType( ); <------------------ How to declare it?
Evaluation eval = new Evaluation(train);
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.
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