German Credit Data Set Arff Download

Search german credit data set, 300 result(s) found Sub set _sum implementation Here you can see an algorithm for clustering, given from the university of Novi Sad, Serbia.

On 22/12/11 8:20 AM, maryam hejazi wrote: > > Salam, > I have two questions: > 1. I use for German credit card dataset with Libsvm and SMO classifier > in weka.

This dataset is imbalance dataset (Positive class data is 700 > and Negative class data is 300). I want to modify this dataset ro > balanced dataset. How can I do this work?

Each visitor will be able to collect their home collection of great music completely free. Download solitude standing suzanne vega rar files. Good music I love it all, although everyone has their own preferences. It is sufficient to choose the desired song and download it.

Weka.filters.supervised.instance.SpreadSubsample can be used to balance the class distribution. If I want to modify this dataset (it is a binary dataset) to > one-class dataset. How can I do this work?

Weka.filters.unsupervised.instance.SubsetByExpression can be used to filter out instances with a particular value for an attribute. After you create a new data set with only one class value present, you will need to edit the ARFF file to remove the no longer present class values from the class attribute declaration in the header of the file.

After the modification binary class dataset to one-class dataset, how > can divide the one-class data set to training and testing data sett? > Please answer to me as soon as possible. Weka.filters.unsupervised.instance.RemovePercentage with, and without, its -V (invert selection) option can achieve this. Cheers, Mark. _______________________________________________ Wekalist mailing list Send posts to: List info and subscription status: List etiquette. Forwarded Message ----- From: Mark Hall To: maryam hejazi; Weka machine learning workbench list.

Persiapan laporan pertanggungjawaban B. Contoh program osis terbaik.

Sent: Friday, December 23, 2011 11:37 AM Subject: Re: [Wekalist] Ask about modify imbalanced data set to balance data set and one-class dataset On 22/12/11 8:20 AM, maryam hejazi wrote: > > Salam, > I have two questions: > 1. I use for German credit card dataset with Libsvm and SMO classifier > in weka.

This dataset is imbalance dataset (Positive class data is 700 > and Negative class data is 300). I want to modify this dataset ro > balanced dataset. How can I do this work?

Weka.filters.supervised.instance.SpreadSubsample can be used to balance the class distribution. If I want to modify this dataset (it is a binary dataset) to > one-class dataset.

How can I do this work? Weka.filters.unsupervised.instance.SubsetByExpression can be used to filter out instances with a particular value for an attribute. After you create a new data set with only one class value present, you will need to edit the ARFF file to remove the no longer present class values from the class attribute declaration in the header of the file. After the modification binary class dataset to one-class dataset, how > can divide the one-class data set to training and testing data sett?

> Please answer to me as soon as possible. Weka.filters.unsupervised.instance.RemovePercentage with, and without, its -V (invert selection) option can achieve this. Cheers, Mark.

_______________________________________________ Wekalist mailing list Send posts to: List info and subscription status: List etiquette. Thank you from your answers. I have questions again about imbalanced binary classification and balanced binary classification dataset.

German credit dataset has 700 positive samples and 300 negative samples; I split the dataset to training dataset with 900 samples and testing dataset with 100 samples. Training dataset has 632 positive samples (genuine) and 268 negative samples (fraud). Testing dataset includes 68 positive samples and 32 negative samples. German credit data dataset is a nominal type dataset. I work with this dataset for support vector machine, but.

3- I use from above defined imbalance datasets for training and testing datasets. Now, I want considering the performnace of training dataset when the reduction of fraud samples (negataive) of training dataset to 20%, 40%, 60% and 80% (e.g. Training dataset has 632 positive sample and 268 negative samples: I remove 268- 20% * 268= 214 negative samples, now I have training dataset with 632 positive samples and 214 negative sample. Again for reduction of 40%: 268-268* 40%= 161 negative dataset, and now I have training dataset with 632 positive sample and 161 negative samples), tasting dataset is used without changing. Now, I get the starnge results when I reduce the negative samples from defined training dataset. I use these training imbalanced datasets defined in above with SMO and LIBSVM with RBF kernel with defualt setting parameter: the accuracy of traing dataset in cresaced when the number of negative samples from inintial tarining dataset is decresed or reduction of negative data samples in initial training set is increased!!!!!!!!!!!!!!!!!!!!!!!!! (e.g. For SMO: accuracy of traing dataset with 20% reduction of negative samples is 74.7% but accuracy of training dataset with 40% reduction of negative samples from initialy imbalanced training dataset is 79.69%., for C-SVC in LIBSVM have the same as problem, it means that with reduction of negative samples in training dataset, accuracy is increased).