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.
An Implementation of Logical Analysis of Data. Boros, E., P.L. Hammer, T. Ibaraki, A. Kogan, E. Mayoraz, I. Muchnik. IEEE Transactions on Knowledge and Data Engineering, vol 12(2), 292-306, 2000
Maximum Patterns in Datasets. Bonates, T.O., P.L. Hammer, A. Kogan. Discrete Applied Mathematics, vol. 156(6), 846-861, 2008.
Classificação Supervisionada de Dados via Otimização e Funções Booleanas. Gomes, V.S.D., T. O. Bonates. Anais do II Workshop Técnico-Científico de Computação, p.21-27, Mossoró, RN, Brazil, 2011.
A jar file is current placed here. One may simple execute the downloaded file either by double clicking or using the following command:
$ java -jar lad-weka.jarWEKA supports full CLI interaction. The following example calls LAD classifier with UCI's vote dataset.
$ java -classpath lad-weka.jar weka.classifiers.rules.LAD -t vote.arff -T vote.arffFor the full LAD's list of options use the following command:
$ java -classpath lad-weka.jar weka.classifiers.rules.LAD -helpPlease, direct any questions, comments, or suggestions to the issues section.