Kansas State University is pleased to announce the release of Machine Learning in Java (MLJ), an open-source software development toolkit for research in machine learning. This toolkit consists of several utility classes whose collective purpose is to facilitate experimentation with inductive learning.

MLJ is governed by the GNU Public License.

The current version, 1.0 alpha (08 May 2002 build), contains the following modules for working with (primarily supervised) inductive learning:

• Java port of MLC++ base inducer and categorizer classes
• Decision tree inducers: ID3, C4.5
• Simple (Naive) Bayes, discrete - improvements cf. [Kohavi, Becker, and Sommerfield, 1996]
• Wrappers for variable selection
• Search-based Feature Subset Selection (FSS)
  [Kohavi and John, 1997]
• CHC genetic algorithm
  [Eshelman, 1990; Guerra-Salcedo and Whitley, 1999]

Because this is the first public version of MLJ, we anticipate many types of inquiries. All comments and feedback, incluidng usage questions and error reports, are welcome. We especially encourage suggestions on improvements and desired features and invite participation by developers of new modules.

If you would like to be part of any discussions relating to the future development of this toolkit, please feel free to join our Yahoo! Group, the MLJ Development Team.