Graph theory has been shown to provide a powerful tool for representing and tackling machine learning problems, such as clustering, semi-supervised learning, and feature ranking. This paper proposes a graph-based discrete differential operator for detecting and eliminating competence-critical instances and class label noise from a training set in order to improve classification performance. Results of extensive experiments on artificial and real-life classification problems substantiate the effectiveness of the proposed approach. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Marchiori, E. (2009). Graph-based discrete differential geometry for critical instance filtering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5782 LNAI, pp. 63–78). https://doi.org/10.1007/978-3-642-04174-7_5
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