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We show how the understandability and speed of genetic programming classification algorithms can be improved, without affecting the classification accuracy. By analyzing the dec...
We introduce a numerical measure on sets of partitions of finite sets that is linked to the Goodman-Kruskal association index commonly used in statistics. This measure allows us t...
It has been observed that traditional decision trees produce poor probability estimates. In many applications, however, a probability estimation tree (PET) with accurate probabilit...
Decision trees are a widely used knowledge representation in machine learning. However, one of their main drawbacks is the inherent replication of isomorphic subtrees, as a result...
Christophe Mues, Bart Baesens, Craig M. Files, Jan...
This work presents decision trees adequate for the classification of series data. There are several methods for this task, but most of them focus on accuracy. One of the requirem...
In this paper we propose an original approach to apply data mining algorithms, namely decision tree-based methods, taking into account not only the size of processed databases but ...
We present a Genetic Programming approach to evolve cooperative controllers for teams of UAVs. Our focus is a collaborative search mission in an uncertain and/or hostile environme...
Marc D. Richards, L. Darrell Whitley, J. Ross Beve...
In medical research, being able to justify decisions is generally as important as taking the right ones. Interpretability is then one of the chief characteristics a learning algor...
Abstract— Decision trees, being human readable and hierarchically structured, provide a suitable mean to derive state-space abstraction and simplify the inclusion of the availabl...
Masoud Asadpour, Majid Nili Ahmadabadi, Roland Sie...
In this paper we propose the "Classification-Based Learning of Subsumption Relations for the Alignment of Ontologies" (CSR) method. Given a pair of concepts from two onto...
Vassilis Spiliopoulos, Alexandros G. Valarakos, Ge...