Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a Multi-Class Problem in Remote Sensing
Abstract
In the present study, we introduce a simple iterative procedure that allows to correct the outputs of a classifier with respect to the new a priori probabilities of a new data set to be scored, even when these new a priori probabilities are unknown in advance. We also show that a significant increase in classification accuracy can be observed when using this procedure properly. More specifically, by applying the correcting procedure to the outputs of a simple logistic regression model, we observe an increase of 5.8% of classification rate on a di#cult real-world multi-class problem -- the automatic labeling of geographical maps based on remote sensing information. Moreover,
Cite
Text
Latinne et al. "Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a Multi-Class Problem in Remote Sensing." International Conference on Machine Learning, 2001.Markdown
[Latinne et al. "Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a Multi-Class Problem in Remote Sensing." International Conference on Machine Learning, 2001.](https://mlanthology.org/icml/2001/latinne2001icml-adjusting/)BibTeX
@inproceedings{latinne2001icml-adjusting,
title = {{Adjusting the Outputs of a Classifier to New a Priori Probabilities May Significantly Improve Classification Accuracy: Evidence from a Multi-Class Problem in Remote Sensing}},
author = {Latinne, Patrice and Saerens, Marco and Decaestecker, Christine},
booktitle = {International Conference on Machine Learning},
year = {2001},
pages = {298-305},
url = {https://mlanthology.org/icml/2001/latinne2001icml-adjusting/}
}