Selective Sampling Algorithms for Cost-Sensitive Multiclass Prediction
Abstract
In this paper, we study the problem of active learning for cost-sensitive multiclass classification. We propose selective sampling algorithms, which process the data in a streaming fashion, querying only a subset of the labels. For these algorithms, we analyze the regret and label complexity when the labels are generated according to a generalized linear model. We establish that the gains of active learning over passive learning can range from none to exponentially large, based on a natural notion of margin. We also present a safety guarantee to guard against model mismatch. Numerical simulations show that our algorithms indeed obtain a low regret with a small number of queries.
Cite
Text
Agarwal. "Selective Sampling Algorithms for Cost-Sensitive Multiclass Prediction." International Conference on Machine Learning, 2013.Markdown
[Agarwal. "Selective Sampling Algorithms for Cost-Sensitive Multiclass Prediction." International Conference on Machine Learning, 2013.](https://mlanthology.org/icml/2013/agarwal2013icml-selective/)BibTeX
@inproceedings{agarwal2013icml-selective,
title = {{Selective Sampling Algorithms for Cost-Sensitive Multiclass Prediction}},
author = {Agarwal, Alekh},
booktitle = {International Conference on Machine Learning},
year = {2013},
pages = {1220-1228},
volume = {28},
url = {https://mlanthology.org/icml/2013/agarwal2013icml-selective/}
}