Optimal constraint-based decision tree induction from itemset lattices

Siegfried Nijssen (1) , Elisa Fromont (2)

1 DTAI - Declarative Languages and Artificial Intelligence
2 LHC - Laboratoire Hubert Curien

In this article we show that there is a strong connection between decision tree learning and local pattern mining. This connection allows us to solve the computationally hard problem of finding optimal decision trees in a wide range of applications by post-processing a set of patterns: we use local patterns to construct a global model. We exploit the connection between constraints in pattern mining and constraints in decision tree induction to develop a framework for categorizing decision tree mining constraints. This framework allows us to determine which model constraints can be pushed deeply into the pattern mining process, and allows us to improve the state-of-the-art of optimal decision tree induction.

Mots clés: Decision tree learning; Formal concepts; Frequent itemset mining; Constraint based mining

Domaines: Apprentissage [cs.LG]

Siegfried Nijssen, Elisa Fromont. Optimal constraint-based decision tree induction from itemset lattices. Data Mining and Knowledge Discovery, 2010, 21 (1), pp.9-51. ⟨10.1007/s10618-010-0174-x⟩. ⟨hal-00499436⟩