Precision-recall space to correct external indices for biclustering

Blaise Hanczar, Mohamed Nadif
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(2):136-144, 2013.

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 this Paper

BibTeX
@InProceedings{pmlr-v28-hanczar13, title = {Precision-recall space to correct external indices for biclustering}, author = {Hanczar, Blaise and Nadif, Mohamed}, booktitle = {Proceedings of the 30th International Conference on Machine Learning}, pages = {136--144}, year = {2013}, editor = {Dasgupta, Sanjoy and McAllester, David}, volume = {28}, number = {2}, series = {Proceedings of Machine Learning Research}, address = {Atlanta, Georgia, USA}, month = {17--19 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v28/hanczar13.pdf}, url = {https://proceedings.mlr.press/v28/hanczar13.html}, 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. } }
Endnote
%0 Conference Paper %T Precision-recall space to correct external indices for biclustering %A Blaise Hanczar %A Mohamed Nadif %B Proceedings of the 30th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2013 %E Sanjoy Dasgupta %E David McAllester %F pmlr-v28-hanczar13 %I PMLR %P 136--144 %U https://proceedings.mlr.press/v28/hanczar13.html %V 28 %N 2 %X 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.
RIS
TY - CPAPER TI - Precision-recall space to correct external indices for biclustering AU - Blaise Hanczar AU - Mohamed Nadif BT - Proceedings of the 30th International Conference on Machine Learning DA - 2013/05/13 ED - Sanjoy Dasgupta ED - David McAllester ID - pmlr-v28-hanczar13 PB - PMLR DP - Proceedings of Machine Learning Research VL - 28 IS - 2 SP - 136 EP - 144 L1 - http://proceedings.mlr.press/v28/hanczar13.pdf UR - https://proceedings.mlr.press/v28/hanczar13.html AB - 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. ER -
APA
Hanczar, B. & Nadif, M.. (2013). Precision-recall space to correct external indices for biclustering. Proceedings of the 30th International Conference on Machine Learning, in Proceedings of Machine Learning Research 28(2):136-144 Available from https://proceedings.mlr.press/v28/hanczar13.html.

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