PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Closed Sets for Labeled Data
Gemma Casas-Garriga, Petra Kralj and Nada Lavrac
PKDD 2006 2006.

Abstract

Closed sets are being successfully applied in the context of compacted data representation for association rule learning. However, their use is mainly descriptive. This paper shows that, when considering labeled data, closed sets can be adapted for prediction and discrimination purposes by conveniently contrasting covering properties on positive and negative examples. We formally justify that these sets characterize the space of relevant combinations of features for discriminating the target class. In practice, identifying relevant/irrelevant combinations of features through closed sets is useful in many applications. Here we apply it to compacting emerging patterns and essential rules and to learn descriptions for subgroup discovery.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2169
Deposited By:Gemma Casas-Garriga
Deposited On:10 August 2006