https://github.com/fracpete/missing-values-imputation-weka-package

Weka package for missing values imputation and injection using various techniques.
https://github.com/fracpete/missing-values-imputation-weka-package

filters java machine-learning plugin preprocessing weka

Last synced: about 1 year ago
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Weka package for missing values imputation and injection using various techniques.

• Host: GitHub
• URL: https://github.com/fracpete/missing-values-imputation-weka-package
• Owner: fracpete
• License: gpl-3.0
• Created: 2014-06-30T23:09:16.000Z (about 12 years ago)
• Default Branch: master
• Last Pushed: 2022-06-29T03:29:12.000Z (about 4 years ago)
• Last Synced: 2024-10-19T12:15:52.202Z (over 1 year ago)
• Topics: filters, java, machine-learning, plugin, preprocessing, weka
• Language: Java
• Homepage:
• Size: 15.7 MB
• Stars: 4
• Watchers: 2
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

          
Missing Values Imputation

=========================

Weka package for missing values imputation (and injection) using various techniques.

The following two filters are available:

* `weka.filters.unsupervised.attribute.MissingValuesImputation` - for imputing missing values

* `weka.filters.unsupervised.attribute.MissingValuesInjection` - for injecting missing values

Imputation

----------

The imputation techniques listed below are available through the 

`weka.filters.unsupervised.attribute.MissingValuesImputation` filter: 

* `NullImputation` - dummy

* `MeansAndModes` - like WEKA's `ReplaceMissingValues` filter

* `MultiImputation` - applies the specified imputation algorithms sequentially

* `SimpleNearestNeighbor` - uses nearest neighbor approach to determine most 

  common label or average (date/numeric)

* `SupervisedPrediction` - predicts missing values in a range of attributes by using regression/classification 

  algorithms built on this attribute subset with the attribute that gets imputed as class attribute and the 

  remainder of the attributes as input variables. 

* `UserSuppliedValues` - simply replaces missing values with user-supplied ones

* `IRMI` - [M. Templ et al (2011): Iterative stepwise regression imputation 

  using standard and robust methods](http://www.statistik.tuwien.ac.at/public/filz/papers/CSDA11TKF.pdf)

  (contributed by [Chris Beckham](https://github.com/christopher-beckham/weka-fimi))

Injection

---------

The injection techniques listed below are available through the 

`weka.filters.unsupervised.attribute.MissingValuesInjection` filter: 

* `NullInjection` - dummy

* `MultiInjection` - applies the specified injection algorithms sequentially

* `AllWithinRange` - set all specified attributes to missing 

* `ClassOnly` - only sets the class values to missing

* `RandomPercentage` - sets random percentage of values in selected attribute range to missing

* `Regex` - replaces strings that match the regular expression in nominal and string attributes

* `Values` - replaces the specified strings in nominal and string attributes 

Releases

--------

Click on one of the following links to download the corresponding Weka package:

* [2022.6.29](https://github.com/fracpete/missing-values-imputation-weka-package/releases/download/v2022.6.29/missing-values-imputation-2022.6.29.zip)

* [2021.10.28](https://github.com/fracpete/missing-values-imputation-weka-package/releases/download/v2021.10.28/missing-values-imputation-2021.10.28.zip)

* [2016.6.12](https://github.com/fracpete/missing-values-imputation-weka-package/releases/download/v2016.6.12/missing-values-imputation-2016.6.12.zip)

* [2016.6.10](https://github.com/fracpete/missing-values-imputation-weka-package/releases/download/v2016.6.10/missing-values-imputation-2016.6.10.zip)

* [2016.6.9](https://github.com/fracpete/missing-values-imputation-weka-package/releases/download/v2016.6.9/missing-values-imputation-2016.6.9.zip)

How to use packages

-------------------

For more information on how to install the package, see:

https://waikato.github.io/weka-wiki/packages/manager/

Maven

-----

Add the following dependency in your `pom.xml` to include the package:

```xml

    

      com.github.fracpete

      missing-values-imputation-weka-package

      2022.6.29

      jar

      

        

          nz.ac.waikato.cms.weka

          weka-dev

        

      

    

```

Please note, when using Maven you may have to register the imputation/injection 

class hierarchies with Weka's GenericObjectEditor if you want to use them in the

GUI as well. See the following files:

* [GenericPropertiesCreator.props](GenericPropertiesCreator.props)

* [GUIEditors.props](GUIEditors.props)