Abstract

C4.5 Competence Map: A Phase Transition-inspired Approach
Nicolas Baskiotis - LRI, Universite Paris-Sud Michele Sebag - LRI, Universite Paris-Sud
How to determine a priori whether a learning algorithm is suited to a learning problem instance is a major scientific and technological challenge. A first step toward this goal, inspired by the Phase Transition (PT) paradigm developed in the Constraint Satisfaction domain, is presented in this paper. Based on the PT paradigm, extensive and principled experiments allow for constructing the Competence Map associated to a learning algorithm, describing the regions where this algorithm on average fails or succeeds. The approach is illustrated on the long andwidely used C4.5 algorithm. A non trivial failure region in the landscape of k-term DNF languages is observed and some interpretations areoffered for the experimental results.

C4.5 Competence Map: A Phase Transition-inspired Approach

Nicolas Baskiotis - LRI, Universite Paris-Sud
Michele Sebag - LRI, Universite Paris-Sud

How to determine a priori whether a learning algorithm is suited to a learning problem instance is a major scientific and technological challenge. A first step toward this goal, inspired by the Phase Transition (PT) paradigm developed in the Constraint Satisfaction domain, is presented in this paper. Based on the PT paradigm, extensive and principled experiments allow for constructing the Competence Map associated to a learning algorithm, describing the regions where this algorithm on average fails or succeeds. The approach is illustrated on the long andwidely used C4.5 algorithm. A non trivial failure region in the landscape of k-term DNF languages is observed and some interpretations areoffered for the experimental results.