abt the prediction.....

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abt the prediction.....

SUFIYAN WARRAICH
with a lot of help from all of u i succeed in making my code run for
traing and testing class thanks alot to u alll....but now i m facing
one other problem.... i want to see the prediction list as i can see
in weka when i trun on the option "output predictions" in which class
i m going to find the code for this... secondly i want to see the rows
as a full when i app the LVQ on it... is there any method exist with
which i can see both training and testing rowzin output...... and
there prediction... thanks a lot....
--
Regards,
Sufiyan Warraich
NUST Institute of Information Technology
Rawalpindi
Pakistan
0321-5186069

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classifier for working out which combinations of attributes give lowest error rate

rich@thevillas.eclipse.co.uk
Hi,

I have a dataset that i've been running through weka. Firstly I used 1R
to determine the least error attribute. Then I ranked the arritbutes
using the default ranker search method. I then used
the attribute evaluator InfoGainAttributeEval to rank each attribute in
terms of the info gain that each give.


But, can I do this on combinations of attributes? Is there anything that
I can run that looks at combinations to see which attributes clump
together well to add to the information gain? Maybe something like
1R,but  that instead of working on a single attribute can work on more
that one so i could find which 2 attributes when combined produce the
lowest error rate?

thanks
Rich

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Re: classifier for working out which combinations of attributes give lowest error rate

subrat
Hi Rich,

A couple of approaches consistent with what you are asking are the Forward and Backward Feature selection procedures. Briefly, these approaches start with either a null set (Forward) or complete (Backward) set of selected attributes and then add or delete feature sets respectively and incrementally.

These methods usually take into account the learning method to be used, and so are 'wrapper' based methods in the sense that they do take the learning algorithm into feature ranking. A different versions of this is the Candidate Elimination/Version Spaces learning.

Also, you may want to try using Genetic Algorithms that take into consideration combinations of attributes in order to select the best ones or their combinations.

I am not sure if these are available in Weka but these are the one you may want to try first. I would also like to know if these are in Weka.

Hope this helps.

Regards,

Subrat





-----Original Message-----
From: [hidden email]
[mailto:[hidden email]]On Behalf Of rich
Sent: Thursday, July 21, 2005 6:01 PM
To: [hidden email]
Subject: [Wekalist] classifier for working out which combinations of
attributes give lowest error rate


Hi,

I have a dataset that i've been running through weka. Firstly I used 1R
to determine the least error attribute. Then I ranked the arritbutes
using the default ranker search method. I then used
the attribute evaluator InfoGainAttributeEval to rank each attribute in
terms of the info gain that each give.


But, can I do this on combinations of attributes? Is there anything that
I can run that looks at combinations to see which attributes clump
together well to add to the information gain? Maybe something like
1R,but  that instead of working on a single attribute can work on more
that one so i could find which 2 attributes when combined produce the
lowest error rate?

thanks
Rich

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Re: classifier for working out which combinations of attributes give lowest error rate

Katrin Tomanek
Hi,

> I am not sure if these are available in Weka but these are the one you may
> want to try first. I would also like to know if these are in Weka.

wrapper-based feature selection can be performed with
weka.attributeSelection.WrapperSubsetEval.

Besides the wrapper, there is weka.attributeSelection.CfsSubsetEval  to
perform attribute subset evaluation independent of the learning scheme.

Both the wrapper and CfsSubsetEval can  be used in combination with
different search methods, including genetic search, exhaustive search etc.

greetings,
Katrin

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Re: classifier for working out which combinations of attributes give lowest error rate

rich@thevillas.eclipse.co.uk
In reply to this post by subrat

Hi Nanda, thanks for the reply

Nanda, Subrat (Research) wrote:

>Hi Rich,
>
>A couple of approaches consistent with what you are asking are the Forward and Backward Feature selection procedures. Briefly, these approaches start with either a null set (Forward) or complete (Backward) set of selected attributes and then add or delete feature sets respectively and incrementally.
>
>  
>
do you know how I can select this in weka? The witten and frank book
suggest that Selective Naive Bayes might be a good option but it's not
listed as a classifier

>These methods usually take into account the learning method to be used, and so are 'wrapper' based methods in the sense that they do take the learning algorithm into feature ranking. A different versions of this is the Candidate Elimination/Version Spaces learning.
>
>Also, you may want to try using Genetic Algorithms that take into consideration combinations of attributes in order to select the best ones or their combinations.
>
>I am not sure if these are available in Weka but these are the one you may want to try first. I would also like to know if these are in Weka.
>  
>

from what I can see and read I don't think there are any genetic
algorithms in weka

thanks
Rich

>Hope this helps.
>
>Regards,
>
>Subrat
>
>
>
>
>
>-----Original Message-----
>From: [hidden email]
>[mailto:[hidden email]]On Behalf Of rich
>Sent: Thursday, July 21, 2005 6:01 PM
>To: [hidden email]
>Subject: [Wekalist] classifier for working out which combinations of
>attributes give lowest error rate
>
>
>Hi,
>
>I have a dataset that i've been running through weka. Firstly I used 1R
>to determine the least error attribute. Then I ranked the arritbutes
>using the default ranker search method. I then used
>the attribute evaluator InfoGainAttributeEval to rank each attribute in
>terms of the info gain that each give.
>
>
>But, can I do this on combinations of attributes? Is there anything that
>I can run that looks at combinations to see which attributes clump
>together well to add to the information gain? Maybe something like
>1R,but  that instead of working on a single attribute can work on more
>that one so i could find which 2 attributes when combined produce the
>lowest error rate?
>
>thanks
>Rich
>
>_______________________________________________
>Wekalist mailing list
>[hidden email]
>https://list.scms.waikato.ac.nz/mailman/listinfo/wekalist
>
>  
>


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