This is the Web page of LAD-WEKA, a Java implementation of the Logical Analysis of Data (LAD) classification algorithm within the WEKA package.

Logical Analysis of Data (LAD) is a rule-based machine learning algorithm based on ideas from Optimization and Boolean Function Theory. The LAD methodology was originally conceived by Peter L. Hammer, from Rutgers University, and has been described and developed in a number of papers since the late 80's. It has also been applied to classification problems arising in areas such as Medicine, Economics, and Bioinformatics. A list with representative publications about LAD will be made available here shortly.

WEKA is an open-source environment for data analysis and machine learning, developed by the Machine Learning group of the University of Waikato, New Zealand. WEKA is the de facto standard for comparing machine learning algorithms and the tool of choice for many data mining and machine learning practitioners. The WEKA package provides access to a range of classification algorithms, including state-of-the-art implementations of SVM, Neural Networks, and Random Forests.

The goal of the LAD-WEKA project is to provide a reference implementation of LAD, which is free, portable, and does not depend on third-party software (such as linear and integer programming solvers). The fact that LAD-WEKA is implemented as a WEKA Classifier allows one to easily run experiments comparing LAD with other classification algorithms available in the WEKA package.

The current version of LAD-WEKA deals exclusively with two-class classification problems containing numerical or binary data (see the To-do list page for an outline of upcoming developments).

LAD-WEKA was developed by Vaux S. D. Gomes and Tibérius O. Bonates and funded by CNPq, the Brazilian Council for Scientific and Technological Development. It is currently available as a stand-alone runnable file (a documented version of the source code might be publicly available in the near future). To run the application, the user must have the Java Runtime Environment (version 1.6 or later) installed in his/her computer. Check the Tutorial page for information on installing and running WEKA on your system.