G
Gemma C. Garriga
Researcher at French Institute for Research in Computer Science and Automation
Publications - 31
Citations - 1259
Gemma C. Garriga is an academic researcher from French Institute for Research in Computer Science and Automation. The author has contributed to research in topics: Clustering coefficient & Matrix (mathematics). The author has an hindex of 13, co-authored 31 publications receiving 1094 citations. Previous affiliations of Gemma C. Garriga include Polytechnic University of Catalonia & Pierre-and-Marie-Curie University.
Papers
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Journal ArticleDOI
Permutation Tests for Studying Classifier Performance
Markus Ojala,Gemma C. Garriga +1 more
TL;DR: The analysis shows that studying the classification error via permutation tests is effective; in particular, the restricted permutation test clearly reveals whether the classifier exploits the interdependency between the features in the data.
Proceedings ArticleDOI
Permutation Tests for Studying Classifier Performance
Markus Ojala,Gemma C. Garriga +1 more
TL;DR: In this paper, the authors explore the framework of permutation-based p-values for assessing the behavior of the classification error and study two simple permutation tests: the first test estimates the null distribution by permuting the labels in the data; this has been used extensively in classification problems in computational biology and the second test produces permutations of the features within classes, inspired by restricted randomization techniques traditionally used in statistics.
Proceedings Article
Randomization Techniques for Graphs
TL;DR: This paper focuses on randomization techniques for unweighted undirected graphs for graph mining within the framework of statistical hypothesis testing, and describes three alternative algorithms based on local edge swapping and Metropolis sampling.
Journal ArticleDOI
Closed Sets for Labeled Data
TL;DR: 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.
Journal ArticleDOI
Banded structure in binary matrices
TL;DR: The results reveal that bands exist in real datasets and that the final obtained orderings of rows and columns have natural interpretations.