Precision-Recall Space to Correct External Indices for Biclustering
Abstract
Biclustering is a major tool of data mining in many domains and many algorithms have emerged in recent years. All these algorithms aim to obtain coherent biclusters and it is crucial to have a reliable procedure for their validation. We point out the problem of size bias in biclustering evaluation and show how it can lead to wrong conclusions in a comparative study. We present the theoretical corrections for all of the most popular measures in order to remove this bias. We introduce the corrected precision-recall space that combines the advantages of corrected measures, the ease of interpretation and visualization of uncorrected measures. Numerical experiments demonstrate the interest of our approach.
Cite
Text
Hanczar and Nadif. "Precision-Recall Space to Correct External Indices for Biclustering." International Conference on Machine Learning, 2013.Markdown
[Hanczar and Nadif. "Precision-Recall Space to Correct External Indices for Biclustering." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/hanczar2013icml-precisionrecall/)BibTeX
@inproceedings{hanczar2013icml-precisionrecall,
title = {{Precision-Recall Space to Correct External Indices for Biclustering}},
author = {Hanczar, Blaise and Nadif, Mohamed},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {136-144},
volume = {28},
url = {https://mlanthology.org/icml/2013/hanczar2013icml-precisionrecall/}
}