Using Domain Knowledge to Systematically Guide Feature Selection

Groves, William

Using Domain Knowledge to Systematically Guide Feature Selection

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Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 3215-3215.

Abstract

The effectiveness of machine learning models can often be improved by feature selection as a pre-processing step. Often this is a data driven process only and can result in models that may not correspond to true relationships present in the data set due to overfitting. In this work, we propose leveraging known relationships between variables to constrain and guide feature selection. Using commonalities across domains, we provide a framework for the user to express model constraints while still making the feature selection process data driven and sensitive to actual relationships in the data.

William Groves. 2013. Using Domain Knowledge to Systematically Guide Feature Selection. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 3215–3215. https://ijcai.org/proceedings/2013/494

@inproceedings{ijcai2013p494,
  title     = {Using Domain Knowledge to Systematically Guide Feature Selection},
  author    = {Groves, William},
  booktitle = {Proceedings of the Twenty-Third International Joint Conference on
               Artificial Intelligence, {IJCAI-13}},
  publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
  editor    = {Francesca Rossi},
  pages     = {3215--3215},
  year      = {2013},
  month     = {8},
  url       = {https://ijcai.org/proceedings/2013/494},
}

TY  - CPAPER
TI  - Using Domain Knowledge to Systematically Guide Feature Selection
AU  - Groves, William
T2  - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13
PB  - AAAI Press / International Joint Conferences on Artificial Intelligence
PY  - 2013
SP  - 3215
EP  - 3215
UR  - https://ijcai.org/proceedings/2013/494
ER  -