Feature selection based on importance and interaction idexes - hierarchical fuzzy rule classifier application

Vincent Bombardier (1) , Laurent Wendling (2) , Emmanuel Schmitt (1)

1 CRAN - Centre de Recherche en Automatique de Nancy
2 LIPADE - EA 2517 - Laboratoire d'Informatique Paris Descartes

This paper proposed an extension of an iterative method to select suitable features for pattern recognition context. The main improvement is to replace its iterative step with another criterion based on importance and interaction indexes, providing suitable feature reduced set. This new scheme is embedded on a hierarchical fuzzy rule classification system. At last, each node gathers a set of classes having a similar aspect. The aim of the proposed method is to automatically extract an efficient subset of suitable features for each node. A selection of features is given. The associated criterion is directly based on importance index and assessment of positive and negative interaction between features. An experimental study, made in a wood defect recognition industrial context, shows the proposed method is efficient to producing significantly fewer rules.

Mots clés: Feature Selection; Pattern Recognition; Fuzzy Rules

Domaines: Traitement du signal et de l'image [eess.SP]; Automatique / Robotique; Traitement du signal et de l'image [eess.SP]

Vincent Bombardier, Laurent Wendling, Emmanuel Schmitt. Feature selection based on importance and interaction idexes - hierarchical fuzzy rule classifier application. 3rd International Joint Conference on Computational Intelligence IJCCI 2011 - International Conference on Fuzzy Computation Theory and Applications, FCTA 2011, Oct 2011, Paris, France. pp.CDROM. ⟨hal-00654132⟩