Graph-Based Discrete Differential Geometry for Critical Instance Filtering

Marchiori, Elena

doi:10.1007/978-3-642-04174-7_5

Graph-Based Discrete Differential Geometry for Critical Instance Filtering

Elena Marchiori

ECML-PKDD 2009 pp. 63-78

doi:10.1007/978-3-642-04174-7_5 /ecmlpkdd/2009/marchiori2009ecmlpkdd-graphbased/

Abstract

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.

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Cite

Text

Marchiori. "Graph-Based Discrete Differential Geometry for Critical Instance Filtering." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009. doi:10.1007/978-3-642-04174-7_5

Markdown

[Marchiori. "Graph-Based Discrete Differential Geometry for Critical Instance Filtering." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2009.](https://mlanthology.org/ecmlpkdd/2009/marchiori2009ecmlpkdd-graphbased/) doi:10.1007/978-3-642-04174-7_5

BibTeX

@inproceedings{marchiori2009ecmlpkdd-graphbased,
  title     = {{Graph-Based Discrete Differential Geometry for Critical Instance Filtering}},
  author    = {Marchiori, Elena},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2009},
  pages     = {63-78},
  doi       = {10.1007/978-3-642-04174-7_5},
  url       = {https://mlanthology.org/ecmlpkdd/2009/marchiori2009ecmlpkdd-graphbased/}
}