Martin | 22 Dec 15:23 2014
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Re: Question about "bagSizePercent" in the Bagging

Good morning,

bagSizePercent it refers to the size of each bag, as a percentage of the training set size.

Regards,
Martin

On 22 December 2014 at 22:07, Mathias Mantelli [via WEKA] <[hidden email]> wrote:
Hello, good morning.

Can someone explain to me the advantages and disadvantages of changing the "bagSizePercent" parameter in combination Bagging method?

Thanks

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Keith Roy | 22 Dec 05:50 2014
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LC

Hi all,

Anybody knows what are the learning curves' types that can be implemented using Weka, and the way to interpret them.

Thanks.
Keith

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Roni Shouval | 21 Dec 14:32 2014
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AUC optimization

Hi 
Im trying to optimize algorithm  parameters using grid search or CVP parameter selection. The problem is that these parameter optimizing algorithms are based on accuracy. The class in my dataset binary is imbalanced (a ratio of 9:1). thus AUC is more suitable as a predictive measure.
I was wondering if there is a way to optimize parameters of algorithms according to AUC?
Thanks
Roni
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Nura Baba | 21 Dec 11:24 2014
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confusion matrix

dear users,

please is there any java code that i can use so as to extract these information (or calculate) from a confusion matrix of a result of any given classifier. the information that i needed to extract from the confusion matrix are the followings:
True negative rate (TNR), true positive rate (TPR), false positive rate (FPR), false negative rate (FNR) and accuracy (ACC).

and output the result in a table format to a Ms Access or any given database application.

the table can be have the following columns.
i.d. of the classifier, TPR, TNR, FPR,FNR, and acc.


thank you
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jason roger | 21 Dec 09:33 2014
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problems with arff format

Hi everyone,

I tried to load my data that used in WEKA into scikit-learn,

Could not find simple way to load my arff file (that I already attached here) into scikit-learn, the problem with finding a clear command(s) to load WEKA arff file because it is difficult to distinguish the class and attributes of arff format within scikit-learn. For this reason I also attached the csv (wish it helps).

I would highly appreciated any help.


Thanks in advance.
Jason
Attachment (Bags.arff): application/octet-stream, 628 bytes
Attachment (Bags.csv): text/csv, 401 bytes
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Martin | 20 Dec 11:33 2014
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Re: location tracking based on signal strengths of wifi hotspots

Perhaps you need to specify the class to be as "location measurement" which is (location measurement 1 and location messurement 2). In addition, the attributes are the features of each location.

Kind Regards,
Martin

On Dec 19, 2014 11:33 PM, "tarscher [via WEKA]" <[hidden email]> wrote:
Hi all,

I'm trying to do location tracking based on signal strengths of wifi hotspots.

A location measurement results in multiple hotspot results. Each hotspot result contains mac address, signal strength and the channel.

example:
Location measurement 1:
|
Hotspot 1 (00197713ec40, -66, 1)
Hotspot 2 (00197713eb40, -40, 6)
Hotspot 4 (00197713ec42, -62, 1)

Location measurement 2:
|
Hotspot 2 (00197713eb40, -66, 11)
Hotspot 5 (00197713eb44, -33, 6)
Hotspot 6 (00197713e882, -92, 2)


I have no clue on how to build my attributes. This looks like a multidimensional dataset but I can't find examples on how to implement this in Weka?

Thanks


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sophie burkhardt | 19 Dec 12:53 2014
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remove filter exception

Hi,

I ran into a problem with the remove filter. Below you find my minimal example and attached is the  arff-file that produces the exception. java.lang.IndexOutOfBoundsException: Index: 1, Size: 1
I think it has to do with the string attribute at the first position but I don't know how to resolve it. If you have any idea I would greatly appreciate it.

Best regards,

Sophie

try {
            Instances data = new Instances(new FileReader("test.arff"));
            int[] indices=new int[]{1};
            Remove remove=new Remove();
            remove.setAttributeIndicesArray(indices);
            remove.setInputFormat(data);
            Instances removed = Filter.useFilter(data, remove);
        } catch (FileNotFoundException e) {
            e.printStackTrace();
        } catch (IOException e) {
            e.printStackTrace();
        } catch (Exception e) {
            e.printStackTrace();
        }
Attachment (test.arff): application/octet-stream, 312 bytes
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valerio jus | 19 Dec 04:55 2014
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Ignore the class

Dear all,

Since in clustering process, the class is not taken into acount (no class). My questions, why in this case got the option "Ignore attributes" in the Cluster panel where the class is one of those attributes that you can ignore?

In addition, is it a good idea always choosing ignore class from the option "Ignore attributes" that is located in the Cluster panel?

Thanks for you.
Valerio

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Martin | 18 Dec 14:39 2014
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Re: Boosting

Good morning Mathias,

The default settings for AdaBoostM1 work well.

In WEKA, to do classification with AdaBoostM1(metalearner), follow the steps:
Open WEKA Explorer -> Classify tab -> Choose button (select weka.classifiers.meta.AdaBoostM1) -> hit "Start"

WEKA will display the results for you (e.g. Precision, Recall, ROC Area, and Confusion Matrix, ...etc.), and all what you have to do is understanding those metrics and considering them in your report.

Upper steps are also same for Bagging, Stacking and Vote.

When you are working with any learning algorithm, you have to know your data type (e.g. numeric or nominal) for both attributes and class, and compare that type with the capabilities of the particular algorithm (they should be matched). 

Here are the capabilities of AdaBoostM1:

Class -- Nominal class, Binary class, Missing class values.

Attributes -- Empty nominal attributes, Unary attributes, Binary attributes, Numeric attributes, Nominal attributes, Missing values, Date attributes.


Good luck and all the best to you.

Best Regards,
Martin

On 18 December 2014 at 20:18, Mathias Mantelli [via WEKA] <[hidden email]> wrote:
Hi all,

One question, in the Weka, Boosting = AdaBoostM1?



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Martin | 18 Dec 13:55 2014
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Re: Boosting

Yes. Boosting is a family of algorithms, and the AdaBoost algorithm is the most influential one. In Weka AdaBoostM1 boost using the AdaBoost M1 method.

Wish this helps.
Martin

On 18 December 2014 at 20:18, Mathias Mantelli [via WEKA] <[hidden email]> wrote:
Hi all,

One question, in the Weka, Boosting = AdaBoostM1?



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Martin | 18 Dec 13:48 2014
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Re: Boosting

Yes. Boosting is a family of algorithms, and the AdaBoost algorithm is the most influential one. In Weka AdaBoostM boost using the AdaBoostM1 method.

Wish this helps.
Martin

On 18 December 2014 at 20:18, Mathias Mantelli [via WEKA] <[hidden email]> wrote:
Hi all,

One question, in the Weka, Boosting = AdaBoostM1?



If you reply to this email, your message will be added to the discussion below:
http://weka.8497.n7.nabble.com/Boosting-tp33187.html
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Gmane