PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

C4.5 Competence Map: a Phase Transition-inspired Approach.
Nicholas Baskiotis and Michele Sebag
In: ICML 2004, 21st International Conference on Machine Learning, july 2004, Banff, Canada.

Abstract

How to determine a priori wether 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 and widely used C4.5 algorithm. A non trivial failure region in the landscape of k-term DNF languages is observed and some interpretations are offered for the experimental results.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:610
Deposited By:Michele Sebag
Deposited On:29 December 2004