Distance-based Decision Tree Learning
2004-2010 The DMIP Group:
Estruch-Gregori,Vicent; Ferri-Ramírez,Cèsar; Hernandez-Orallo,José; Martínez-Plumed,Fernando; Ramirez-Quintana,M.José
DBDT is a machine learning algorithm that integrates decision tree learning and center splitting. Roughly speaking, the inferred classifer can be viewed as a tree of attribute prototypes (The value distribution of an attribute is represented by a set of prototypes.). An instance is linked to one prototype or other depending on its proximity.
ProbDBDT is a variation of DBDT that uses probabilities-based distances.
Newton Trees based on DBDT framework, is a redefinition of probability estimation trees (PET) based on a stochastic understanding of decision trees that follows the principle of attraction (relating mass and distance through the Inverse Square Law). The structure, application and, very especially, the graphical representation of these Newton trees provide a way to make their stochastically driven predictions compatible with user's intelligibility, so preserving one of the most desirable features of decision trees, comprehensibility.
You can download the whole system package for academic use with the following conditions:
DISCLAIMER & COPYRIGHT: The software has been checked on a several Intel-based machines (PCs) under different versions of Ms. Windows (2000,XP). In this regard, you can make any modification to the software, provided you always make the changes explicit and refer to its original authors. Obviously, we are not responsible for any damage caused by the use or misuse of this software. If you find any bug please contact the authors. For commercial use *do* contact the authors.
Many example datasets in DBDT format (*.arff file + metric_space.txt) can be found here . If you have no examples in DBDT format, please download them because they will be required.
After loading and runing the Java project, the look should be as follows.
© 2004-2010 Vicent Estruch-Gregori , Fernando Martínez Plumed