On the Stratification of Multi-Label Data
Abstract
Stratified sampling is a sampling method that takes into account the existence of disjoint groups within a population and produces samples where the proportion of these groups is maintained. In single-label classification tasks, groups are differentiated based on the value of the target variable. In multi-label learning tasks, however, where there are multiple target variables, it is not clear how stratified sampling could/should be performed. This paper investigates stratification in the multi-label data context. It considers two stratification methods for multi-label data and empirically compares them along with random sampling on a number of datasets and based on a number of evaluation criteria. The results reveal some interesting conclusions with respect to the utility of each method for particular types of multi-label datasets.
Cite
Text
Sechidis et al. "On the Stratification of Multi-Label Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011. doi:10.1007/978-3-642-23808-6_10Markdown
[Sechidis et al. "On the Stratification of Multi-Label Data." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2011.](https://mlanthology.org/ecmlpkdd/2011/sechidis2011ecmlpkdd-stratification/) doi:10.1007/978-3-642-23808-6_10BibTeX
@inproceedings{sechidis2011ecmlpkdd-stratification,
title = {{On the Stratification of Multi-Label Data}},
author = {Sechidis, Konstantinos and Tsoumakas, Grigorios and Vlahavas, Ioannis P.},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2011},
pages = {145-158},
doi = {10.1007/978-3-642-23808-6_10},
url = {https://mlanthology.org/ecmlpkdd/2011/sechidis2011ecmlpkdd-stratification/}
}