I am glad to present the Learning Classifier System Open Repository (LCSOR); a repository on LCSs implemented in C++ to further extend these family of machine learning techniques. The files are stored in SourceForge.

The project is divided in three distinct parts: (1) the source code (a tarball for each algorithm), (2) the problem environment, and (3) the documentation in the form of doxygen html.

To start I uploaded the first version of the sUpervised Classifier System (UCS). The current version of this algorithm is 0.98.27, which implements the real interval-based representation (more precisely the unordered-bound representation) and the categorial representation of UCS. Also, the imbalanced estimators are included (see (Orriols-Puig, 2008)). I have codified some other measures of quality: Cohen's Kappa score, precision, recall and the F-Measure (F1).

As you see, this project is just in its infancy and will grow little by little. 

Algorithms included so far:

 - UCS 0.98.27 (SourceProblem environmentDocumentation)

 This new version (9/27/2014) incorporates:
 - Minor bug fixes.

Installation details
In order to compile and install this software follow these steps:

  1. download the source code (e.g., UCS.tar.gz),
  2. uncompress the file using tar ($ tar xvzf UCS.tar.gz),
  3. enter to the directory containing the source code and compile it ($ make),
  4. download the problem environment (e.g.,,
  5. uncompress the file using tar ($ tar xvjf, and
  6. execute the program (e.g, $ ./UCS ../Problems/tao/config/tao.cfg).

Citation details

If you use LCSOR for your research, please cite it with the following:

    author = "Sancho-Asensio, Andreu",
    year = "2014",
    title = "Learning classifier system open repository",
    url = "",
    institution = "Ramon Llull University" 

If you have any comments or find any bug, please send a message!


Orriols-Puig, Albert: "New challenges in learning classifier systems: mining rarities and evolving fuzzy models," PhD Thesis, La Salle, Universitat Ramon Llull,  2008.


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