Using domain knowledge to systematically guide feature selection

Authors:
William Groves
Affiliations:
Computer Science and Engineering, University of Minnesota
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

Citing 3
Cited 0

Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning

ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
To buy or not to buy: mining airfare data to minimize ticket purchase price

Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining

Quantified Score

Hi-index	0.00

Visualization

Abstract

The effectiveness of machine learning models can often be improved by feature selection as a preprocessing 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.