This project is a WEKA (Waikato Environment for Knowledge Analysis) compatible implementation of MODLEM - a Machine Learning algorithm which induces minimum set of rules. These rules can be adopted as a classifier (in terms of ML). It is a sequential covering algorithm, which was invented to cope with numeric data without discretization. Actually the nominal and numeric attributes are treated in the same way: attribute's space is being searched to find the best rule condition during rule induction. In result numeric attribute's conditions are more precise and closely describe the class. This algorithm contains some aspects of Rough Set Theory: the class definition can be described accordingly to its lower or upper approximation. For more information, see: Stefanowski, Jerzy. The rough set based rule induction technique for classification problems. In: Proc. 6th European Congress on Intelligent Techniques and Soft Computing, vol. 1. Aachen, 1998. s. 109-113.
This version of MODLEM does not consider an important condition for developing decision rules from noisy datasets. As described in "J. Stefanowski, On combined Classifier, Rule Induction and Rough Sets, Transactions on rough sets VI, Pages 329-350" page 335, 2nd paragraph.".... For some datasets - in particular noisy ones - using this stop condition ([T] \subseteq B) may produce too specific rules. In such situations the user may accept partially discriminating rules with high enough accuracy, - this can be done by applying another stop condition [T \cup B]/ [T] >= \alpha..." It will be very useful if this Weka version includes the above modified stopping condition for the decision rule, allowing the user to select the value for the alpha parameter and therefore induce more general rules.