webdata_wXa

**Author**: John Platt
**Source**: [libSVM](http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets) - Date unknown
**Please cite**: John C. Platt.
Fast training of support vector machines using sequential minimal optimization.
In Bernhard Schölkopf, Christopher J. C. Burges, and Alexander J. Smola, editors, Advances in Kernel Methods - Support Vector Learning, Cambridge, MA, 1998. MIT Press.a

This is the famous webdata dataset w[1-8]a in its binary version, retrieved 2014-11-14 from the libSVM site. Additional to the preprocessing done there (see LibSVM site for details), this dataset was created as follows:

* load all web data datasets, train and test, e.g. w1a, w1a.t, w2a, w2a.t, w3a, ...
* join test and train for each subset, e.g. w1a and w1a.t, w2a and w2a.t
* normalize each file columnwise according to the following rules:
* If a column only contains one value (constant feature), it will set to zero and thus removed by sparsity.
* If a column contains two values (binary feature), the value occuring more often will be set to zero, the other to one.
* If a column contains more than two values (multinary/real feature), the column is divided by its std deviation.
* afterwards all these 8 files are merged into one, and randomly sorted.
* duplicate lines were finally removed.

An R script which does all of these steps can be found here:
https://github.com/openml/data_scripts/blob/master/webdata_wXa/dataDownloader.R