Conceptual Set Covering: Improving Fit-and-Split Algorithms
Abstract
Many learning systems implicitly use the fit-and- split learning method to create a comprehensive hypothesis from a set of partial hypotheses. At the core of the fit-and-split method is the assignment of examples to partial hypotheses. To date, however, this core has been neglected. This paper provides the first definition and model of the fit-and-split assignment problem. Extant systems perform assignment nearly arbitrarily, implicitly using, for example, greedy set covering. This paper also presents Conceptual Set Covering (CSC), a new assignment algorithm. An extensive empirical evaluation over a wide range of learning problems suggests that CSC can improve any fit-and-split learning system.
Cite
Text
Kadie. "Conceptual Set Covering: Improving Fit-and-Split Algorithms." International Conference on Machine Learning, 1990. doi:10.1016/B978-1-55860-141-3.50009-2Markdown
[Kadie. "Conceptual Set Covering: Improving Fit-and-Split Algorithms." International Conference on Machine Learning, 1990.](https://mlanthology.org/icml/1990/kadie1990icml-conceptual/) doi:10.1016/B978-1-55860-141-3.50009-2BibTeX
@inproceedings{kadie1990icml-conceptual,
title = {{Conceptual Set Covering: Improving Fit-and-Split Algorithms}},
author = {Kadie, Carl Myers},
booktitle = {International Conference on Machine Learning},
year = {1990},
pages = {40-48},
doi = {10.1016/B978-1-55860-141-3.50009-2},
url = {https://mlanthology.org/icml/1990/kadie1990icml-conceptual/}
}