DM: Paper: Data Mining using MLC++, A Machine Learning Library in C++

[Ronny Kohavi]

The journal version of the paper "Data Mining using MLC++, A Machine
Learning Library in C++," which received the IEEE Ramamoorthy best 
paper
award at Tool with AI '96 was accepted to IJAIT, the International
Journal on AI Tools.

A copy of the expanded paper can be found in
   http://robotics.stanford.edu/users/ronnyk/
under publications.



                            ABSTRACT

Data mining algorithms including machine learning, statistical
analysis, and pattern recognition techniques can greatly improve our
understanding of data warehouses that are now becoming more
widespread.  In this paper, we focus on classification algorithms and
review the need for multiple classification algorithms.  We describe a
system called MLC++, which was designed to help choose the appropriate
classification algorithm for a given dataset by making it easy to
compare the utility of different algorithms on a specific dataset of
interest.  MLC++ not only provides a workbench for such comparisons,
but also provides a library of C++ classes to aid in the development
of new algorithms, especially hybrid algorithms and multi-strategy
algorithms.  Such algorithms are generally hard to code from scratch.
We discuss design issues, interfaces to other programs, and
visualization of the resulting classifiers.


--

   Ronny Kohavi (ronnyk@sgi.com, http://robotics.stanford.edu/~ronnyk)