Michael Hall | 28 Mar 14:59 2015
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Checking current Weka version in use

Trying to come up with some quick command line way to check this from an application of mine (command line) I Googled up…
weka.core.Version
3.7.11

-1? -1
 0? 0
+1? 1

comparing with 5.0.1
isOlder? true
equals ? false
isNewer? false

comparing with 3.7.11
isOlder? false
equals ? true
isNewer? false

Which looks fine except seems to be printing out more debug/logging information than I need?

Michael Hall



AppConverter convert Apple jvm to openjdk apps http://www195.pair.com/mik3hall/index.html#appconverter




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CARLOS MARTINEZ CORTES | 27 Mar 23:37 2015
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New attribute selector

Hello, I attached the package´s Description.props file of a new attribute selector search algorithm which I create and I want to do official in Weka, thanks for attention.

# Template Description file for a Weka package
# 

# Package name (required)
PackageName=MultiObjetiveEvolutionarySearch

# Version (required)
Version=1.0.0

#Date (year-month-day)
Date=2015-03-27

# Title (required)
Title=An Multi-objective Evolutionary Algorithm (MOEA) to explore the attribute space.

# Category (recommended)
Category=Attribute selection

# Author (required)
Author=Carlos Martinez Cortés<carlos.martinez6 <at> um.es>, Fernando Jiménez
Barrionuevo<fernan <at> um.es>, Gracia Sánchez Carpena <gracia <at> um.es>

# Maintainer (required)
Maintainer=Carlos Martinez Cortés<carlos.martinez6 <at> um.es>

# License (required)
License=GPL 2.0

# Description (required)
Description=MultiobjectiveEvolutionarySearch explores the attribute space using the ENORA
Multi-objective Evolutionary Algorithm. Two objectives are optimized. The first one is to be maximized
and it is chosen by the evaluator. The second one is to minimize the subset cardinality.The non-dominated
solution in the last population with the best fitness for the first objective is shown as
output.<br/><br/>ENORA is an elitist Pareto-based multi-objective evolutionary algorithm that uses
a(mu+lambda) survival with the following operators:<br/><br/> - Uniform random initialization.
<br/> - Binary tournament selection. <br/> - Ranking based on local non-domination level with crowding
distance. <br/> - Self-adaptive uniform crossover. <br/> - Self-adaptive one-bit flip mutation.
<br/><br/>For more information about ENORA see: <br/><br/>Jiménez, F., Sánchez, G. & Juárez, J.M.
(2014). Multi-objective evolutionary algorithms for fuzzy classification in survival prediction.
Artificial Intelligence in Medicine, 60(3), 197-219.

# Package URL for obtaining the package archive (required)
PackageURL=https://sourceforge.net/projects/moea/files/MultiObjetiveEvolutionarySearch1.0.0.zip/download

# Dependencies (format: packageName (equality/inequality version_number)
Depends=weka (>=3.7.12)
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CARLOS MARTINEZ CORTES | 27 Mar 18:49 2015
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help to contributing a package

I want to contribute with a new package to weka and I create a desciption.props file, but I don´t find the contact of the current weka maintainer. 

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Slomka, Piotr | 27 Mar 00:43 2015

10-fold cross validation in WEKA

I wanted to clarify how 10-fold cross-validation is done in Weka. When we output prediction estimates   (-p option in CLI)  and the 10-fold CV is selected,

are the individual prediction estimates  in the text output aggregated from 10 different folds ? Is there a way to identify which fold, which model was used for a given prediction for a given case?

 

Also following up on the issues of repeated 10-fold validation.  Is changing the seed 10 times (for example from 0- to 10) in the model parameters and then running the 10-fold

CV 10 times with different seeds outputting prediction estimates 10 times and then averaging the probability estimates from 10 runs for each case

would be a correct way to estimate performance with repeated 10-fold validation in WEKA ?  Note that we need to get actual prediction estimates for each case

to evaluate the performance and compare performance outside of WEKA.

Best regards, Piotr

 

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Alberto Castori | 26 Mar 15:10 2015
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WEKA how to infer training set structure from a Classifier

Hello guys,

I am in th follow situation:
- I have buit a training set with this attributes: id numeric, 
myattribute string, class nominal
- I trained and built a model, next saved it in a file .model
All of these actions are done from Weka Explorer.

After all using Weka api from Java, I loaded in my application the 
Classifier using an ObjectInputStream.
I would like to know if there is a way from infer the structure of the 
training set (es. how many attributes are in training set and theri 
types) used from the Classifier object.
If there is, how I do it ?
It could be useful to me for building a test on the fly without knowing 
nothing about training set.

Regards

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Parashara Ramsees | 25 Mar 21:35 2015
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How to generate multidimensional data with specific clustering properties?


 

<!-- .ExternalClass .ecxhmmessage P { padding:0px; } .ExternalClass body.ecxhmmessage { font-size:12pt; font-family:Calibri; } -->

In section 5.A of a research paper the researcher used the following synthetic datasets:

  1. GAUSS consisted of six Gaussian clusters with identity covariance, each with 500 points in five dimensions. Their means were randomly assigned a value from zero to 10 in each dimension. Cluster means were required to be at least four Euclidean distance apart, and points were required to within two Euclidean distance of their cluster mean.
  2. PAIRED consisted of three pairs of Gaussian clusters with identity covariance, each with 500 points in five dimensions. Each pair of Gaussians was placed around a mean with a randomly assigned value in each dimension from zero to 20 such that the Euclidean distance between paired Gaussian clusters was between four and eight, and the Euclidean distance between non-paired Gaussians was at least 12. Additionally, points were required to be within two Euclidean distance of their cluster mean.

  3. ELONG consisted of five Gaussian clusters with identity covariance, each with 300 points in five dimensions. Their means were randomly assigned a value from zero to 50 in each dimension. To create elongated clusters in different dimensions, we multiplied the values of a single, distinct dimension for each cluster by 15. Cluster means were required to be at least five Euclidean distance apart.

  4. UNIFORM consisted of eight clusters, each with 300 points in three dimensions. Each cluster had its points uniformly distributed in a 3x3x3 box around a randomly assigned center in a 10x10x10 cube. Cluster centers were required to be five Euclidean distance apart.
  5. RINGS consisted of 2 ring clusters centered around (0,0), a larger outer ring with radius 2 and a smaller inner ring of radius 1. 400 points were evenly spaced by degrees on the inner ring.

I don't have these datasets. I tried to contact the researcher but of no use.

How to create these datasets? Is there any kind of tool to create them?


Original Paper is found here.

Thanks
Parashara
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ajitha padmanabhan | 25 Mar 18:30 2015
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classifiers

. hi

Thanks peter. Now weka server running , but I got the following
"[Weka]associationRulesVisualizer can't be loaded because com.sun.j3d.utils.Universe.SimpleUniverse can't be instantiated
... similar like this for associationRulesViewer...."

Do provide me with this solution

rgds


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Tarfa | 25 Mar 17:38 2015
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HSIC feature selection

I am wondering if there is any feature selection method in Weka that employs
HSIC feature selection criterion?
Thanks,

--
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Gaurav Pandey | 24 Mar 22:27 2015
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Interpreting saved SVM model

Hi,

I am using the SMO classifier for classifying my dataset and am saving the model learnt using the -d option (not as XML). However, I am unable to open the saved model file in any text-friendly editor. I wish to automatically parse the details of this saved model, especially the weights of the features, for further analysis, so getting the model in some kind of text-friendly format is quite important for me. Is this possible in Weka, or using some auxiliary tool? Please let me know.

Thanks,

Gaurav

--
Gaurav Pandey, Ph.D.                                  
Assistant Professor
Institute for Genomics and Multiscale Biology
Department of Genetics and Genomic Sciences
Mount Sinai School of Medicine, New York City
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ajitha padmanabhan | 24 Mar 17:55 2015
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weka server

hi
i am using windows 7 64 bit, and weka server not running.
c:\program files\weka-3-7> java weka.Run WekaServer 

i got the error "Error:could not find or load main class weka.Run

sincerely
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ajitha padmanabhan | 24 Mar 17:22 2015
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weka server

hi
I am using weka 3.7.12, i am trying to run weka server by
using

C:\program files\weka-3-7>java weka.Run WekaServer -host localhost -port 8085

error i got was "could not find or load main class weka.Run.

i already loaded weka server through package manager. as already said set the class path including weka.jar

why this error.Do provide me with the solution.

FYI: I already worked this with weka 3.7.11 perfectly.I formatted the system, again and installed weka 3.7.12  so that it have extensive support to distributed data mining. I have only weka 3.7.12 now, and i got the above mentioned problem while running weka server

Do pls any one give me the solution.

rgds


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Gmane