Haluk Dogan | 19 Apr 20:09 2014
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bayesian network continuous data generation

Hi,

I could generate the data for given Bayesian Network (BN). The generated data is binary, but I want to measure the distortion. Therefore, I need continuous values.

Is there a way to generate the continuous data for given BN?

Thanks.

--
HD
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Ali Alrazgi | 19 Apr 12:28 2014
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Multi classes

Dear Wekalist
I would like to classify data with multi-classes (in my case i have two)
my datset contains two classes, class one contains yes and no, the second class contains A and B
how can i classify this data
best regards 

 


With All Best Wishes and regards
 Ali M. Alraziqi
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RazorAlliance192 | 18 Apr 21:35 2014
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Make own implementation of Latent Dirichlet Allocation

I wish to implement LatentDirichletAllocation (LDA) in WEKA by means of a
filter. However I am a bit confused which filter I need to use (default or
batch). First I will use WEKA to get my dataset after which I will apply LDA
using an external library JGibbLDA. In order for this library to be able to
perform LDA, the data needs to be transformed in a specific format. I have
already managed to do this but now I wish to estimate LDA.

I am currently using SimpleBatchFilter and in the method process() I call a
method that performs the estimation on my dataset. However, it turns out
that process() is being called as many times as I have instances in my
dataset.

Is there a way where I can make a filter that only calls process() once?

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congmin min | 18 Apr 17:42 2014
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"display" button in Weka SMOTE UI can't be applied.


Hello, I am using Weka Explorer to do a SMOTE oversampling of my training data. The file format is libsvm compatible. It's loaded. Everything looks fine. But when I select filter->supervised learning->SMOTE, and choose the default settings. Then I should press "apply" to start sampling, but the problem is that the "apply" button is gray, disabled. I can't do it.


What might cause this? Really thanks for any information.

Martin
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Mercedeh Shahrashoob | 18 Apr 11:04 2014
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Some Evaluation of algorithms

hi advances;

I want 
"Training Time",
"Classification time",
"Testing Time" and
"Memory usage"
for each algorithm of classify tab;

please help me...

Thank you very much.

--
Cheers;
Mercedeh Shahrashoob.
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Soubhik Paul | 18 Apr 05:46 2014
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How to interpret Weka logistic regression output for a nominal attribute and its coefficients?

Kindly help me interpreting the output of logistic regression in Weka.
I have a data set. Below is the `arff` file. There is one numeric attribute and 3 nominal attributes. Risk is the class (`H`= high, `L`= low):

    <at> attribute Age real
    <at> attribute 'Education status' {Illiterate,Literate}
    <at> attribute 'Residence' {Rural,Urban,'Semi rural'}
    <at> attribute 'Current Marrital Status' {Married,Widow,Divorced}
    <at> attribute Risk {H,L}

    <at> data
    25,Literate,Urban,Married,L
    30,Illiterate,Urban,Married,L
    45,Literate,Rural,Married,H
    45,Illiterate,'Semi rural',Married,L
    50,Literate,Rural,Married,H
    55,Literate,Urban,Widow,L
    ......
   
    Logistic Regression with ridge parameter of 1.0E-8
     Coefficients...
                                                         Class
                                                           H 
    ================================================================

    Age------------------------------------------------2.1568
    Education status-----------------------------------0.1056
    Residence=Rural------------------------------------1.4934
    Residence=Urban------------------------------------0.5399
    Residence=Semi rural-------------------------------15.6246
    Current Marrital Status=Married--------------------7.9315
    Current Marrital Status=Widow----------------------9.9869
    Current Marrital Status=Divorced-------------------7.7076
    Intercept------------------------------------------9.4682      

If I want to build the equation from theses coefficients and use the attributes value for a new case,
how should I set the value of the variable `Education status` (its value is either `Literate` or `Illiterate`) so
that for a new case where the attribute `Education` status is `Literate`, what value should I use in the `Education status` variable
for the factor `-0.1056*Education` status in the equation? How does Weka do this? Should I use `1` for literate and `0` for illiterate?
For nominal attributes with two possible values, the coefficient is not available for each such value, unlike a nominal attribute with >2
possible values.
So, what value should I use while manually using the equation formed from the coefficients supplied by Weka?
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swapnajit_chakraborti | 18 Apr 15:57 2014
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serialversionUID

Hi,

When we define a new weka class derived from some existing
class, how do we select which number to assign to variable
serialVersionUID for this new class?

Regards,
Swapnajit

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swapnajit_chakraborti | 18 Apr 15:25 2014
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getClusterCentroids() method

Hi,

I am using weka 3.6. I am not able to access getClusterCentroids() method
for a SimpleKMeans clusterer object. But in eclipse expansion of weka.jar
file this particular function is displayed.
What may be the issue?

Thanks for your help.

Regards,
Swapnajit

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Deepa | 17 Apr 21:13 2014
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Re: Bagging with heterogenous classifiers

Hi,
    How to Use bagging method  with heterogenous classifiers (decision tree, decision rules, naive bayes classifiers ) on a classification data set.And how do I Compare the performance of the individual classifiers and the bagged classfier.


On Fri, Apr 18, 2014 at 12:37 AM, deepa as <[hidden email]> wrote:


---------- Forwarded message ----------
From: deepa as <[hidden email]>
Date: Fri, Apr 18, 2014 at 12:34 AM
Subject: Bagging with heterogenous classifiers
To: [hidden email]


Hi,
    How to Use bagging method  with heterogenous classifiers (decision tree, decision rules, naive bayes classifiers ) on a classification data set.And how do I Compare the performance of the individual classifiers and the bagged classfier.



View this message in context: Re: Bagging with heterogenous classifiers
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Deepa | 17 Apr 21:08 2014
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Fwd: Bagging with heterogenous classifiers



---------- Forwarded message ----------
From: deepa as <[hidden email]>
Date: Fri, Apr 18, 2014 at 12:34 AM
Subject: Bagging with heterogenous classifiers
To: [hidden email]


Hi,
    How to Use bagging method  with heterogenous classifiers (decision tree, decision rules, naive bayes classifiers ) on a classification data set.And how do I Compare the performance of the individual classifiers and the bagged classfier.


View this message in context: Fwd: Bagging with heterogenous classifiers
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saff safa | 17 Apr 20:34 2014
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select attributes

hello I need to find they are the most interesting attributes of all my
attributes. I try to use "select attributes" but don't understand the roles
of 'attribut evaluator' and 'search method' can you help me .

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Gmane