PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Closed Sets for Labeled Data
Gemma Garriga, Petra Kralj and Nada Lavrac
Journal of Machine Learning Research Volume 9, Number 4, pp. 559-580, 2008.

Abstract

Closed sets have been proven successful in the context of compacted data representation for association rule learning. However, their use is mainly descriptive, dealing only with unlabeled data. This paper shows that when considering labeled data, closed sets can be adapted for classification and discrimination purposes by conveniently contrasting covering properties on positive and negative examples. We formally prove 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: to compact emerging patterns of typical descriptive mining applications, to reduce the number of essential rules in classification, and to efficiently learn subgroup descriptions, as demonstrated in real-life subgroup discovery experiments on a high dimensional microarray data set.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:5130
Deposited By:Jan Rupnik
Deposited On:24 March 2009