Supervised feature evaluation by consistency analysis: application to measure sets used to characterise geographic objects

Patrick Taillandier (1) , Alexis Drogoul (1, 2)

1 UMMISCO - Unité de modélisation mathématique et informatique des systèmes complexes [Bondy]
2 MSI - Modélisation et Simulation Informatique de systèmes complexes

Nowadays, supervised learning is commonly used in many domains. Indeed, many works propose to learn new knowledge from examples that translate the expected behaviour of the considered system. A key issue of supervised learning concerns the description language used to represent the examples. In this paper, we propose a method to evaluate the feature set used to describe them. Our method is based on the computation of the consistency of the example base. We carried out a case study in the domain of geomatic in order to evaluate the sets of measures used to characterise geographic objects. The case study shows that our method allows to give relevant evaluations of measure sets.

Mots clés: Supervised feature evaluation; consistency computation; geomatic

Domaines: Intelligence artificielle [cs.AI]

Patrick Taillandier, Alexis Drogoul. Supervised feature evaluation by consistency analysis: application to measure sets used to characterise geographic objects. International Conference on Knowledge and Systems Engineering, 2010, Hanoi, Vietnam. pp.63-68. ⟨hal-00689123⟩