Maximum Likelihood Rule Ensembles (MLRules) is a new rule induction algorithm for solving classification problems via probability estimation. The ensemble is built using boosting, by greedily minimizing the negative loglikelihood which results in estimating the class conditional probability distribution. The main advantage of decision rules is their simplicity and comprehensibility: they are logical statements of the form "if condition then decision", which is probably the easiest form of model to interpret. On the other hand, by exploiting a powerful statistical technique to induce the rules, the final ensemble has very high prediction accuracy.
Fork of the original code located here: http://www.cs.put.poznan.pl/wkotlowski/software-mlrules.html
@article{DemKotSlo08MLRules,
author = {Krzysztof Dembczy\'nski and Wojciech Kot{\l}owski and Roman S{\l}owi\'nski},
title = {Maximum likelihood rule ensembles},
booktitle = {Proceedings of the 25th International Conference on Machine Learning (ICML 2008)},
year = {2008}
}
- Turned into a Weka package
- Uses a seeded random number generator now (superclass:
weka.classifiers.RandomizableClassifier)
Use the following dependency in your pom.xml:
<dependency>
<groupId>com.github.fracpete</groupId>
<artifactId>mlrules-weka-package</artifactId>
<version>2023.7.26</version>
<type>jar</type>
<exclusions>
<exclusion>
<groupId>nz.ac.waikato.cms.weka</groupId>
<artifactId>weka-dev</artifactId>
</exclusion>
</exclusions>
</dependency>For more information on how to install the package, see: