Using Multiple Models to Understand Data
Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence. Pages 1723-1727.
Abstract
A human’s ability to diagnose errors, gather data, and generate features in order to build better models is largely untapped. We hypothesize that analyzing results from multiple models can help people diagnose errors by understanding relationships among data, features, and algorithms. These relationships might otherwise be masked by the bias inherent to any individual model. We demonstrate this approach in our Prospect system, show how multiple models can be used to detect label noise and aid in generating new features, and validate our methods in a pair of experiments.
@inproceedings{ijcai2011p289,
title = {Using Multiple Models to Understand Data},
author = {Patel, Kayur and Drucker, Steven Mark and Fogarty, James and Kapoor, Ashish and Tan, Desney S.},
booktitle = {Proceedings of the Twenty-Second International Joint Conference on
Artificial Intelligence, {IJCAI-11}},
publisher = {AAAI Press/International Joint Conferences on Artificial Intelligence},
editor = {Toby Walsh},
pages = {1723--1727},
year = {2011},
month = {7},
doi = {10.5591/978-1-57735-516-8/IJCAI11-289},
url = {https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-289},
}
TY - CPAPER TI - Using Multiple Models to Understand Data AU - Patel, Kayur AU - Drucker, Steven Mark AU - Fogarty, James AU - Kapoor, Ashish AU - Tan, Desney S. T2 - Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, IJCAI-11 PB - AAAI Press/International Joint Conferences on Artificial Intelligence PY - 2011 SP - 1723 EP - 1727 DO - 10.5591/978-1-57735-516-8/IJCAI11-289 UR - https://doi.org/10.5591/978-1-57735-516-8/IJCAI11-289 ER -Copied
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