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Nearest Neighbor Classification

 

               Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study - Complementary Material

This Website contains additional material to the SCI2S research paper on Prototype Selection

S. García, J. Derrac, J.R. Cano and F. Herrera, Prototype Selection for Nearest Neighbor Classification: Taxonomy and Empirical Study.
IEEE Transactions on Pattern Analysis and Machine Intelligence 34:3 (2012) 417-435 doi: 10.1109/TPAMI.2011.142   iconPdf.png

Summary:

  1. Abstract
  2. Survey of PS methods
  3. Experimental study
  4. JAVA code for PS methods
    1. Source Code
    2. KEEL version with PS package

JAVA code for PS methods

In this section, we provide the full package of PS algorithms employed in the experimental study, available for public use. It is composed by the source code of the methods and a modified version of the KEEL software tool with the PS package and the k-NN classifier integrated.

Source code

The source code of the PS package can be downloaded from here.

It is written in the Java programming language. Although it is developed to be run inside the KEEL environment, it can be also executed on a standard Java Virtual Machine. To do this, it is only needed to place the training datasets at the same folder, and write a script which contains al the relevant parameters of the algorithms (see the KEEL reference manual, section 3; located under the KEEL Software Tool item of the main menu).

KEEL version with PS package

The KEEL version with PS package can be downloaded from here.

The complete KEEL user manual can be downloaded from here.

Esentially, to generate a experiment with this modified version of KEEL, it is needed to perform the following steps:

- Execute the KEEL GUI with the command: java -jar GraphInterKeel.jar.

- Select Experiments.

- Select Classification (it is recomended the use of 10-fold cross validation, as set by default).

- Select the data sets desired to use, and click on the main window to place them.

- Click on the second icon of the left bar, select the IS method desired, and click on the main window to place it.

- Click on the third icon of the left bar, select the KNN classifier, and click on the main window to place it.

- Click on the last icon of the left bar, and create some arcs which joins all the nodes of the experiments.

- Click on the Run Experiment icon, located at the top bar.

This way, it is possible to generate an experiment ready to be executed on any machine with a Java Virtual Machine installed.


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