Causal Mechanisms and Classification Trees for Predicting Chemical Carcinogens

Louis Anthony Cox Jr
Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, PMLR R2:18-26, 1999.

Abstract

Classification trees, usually used as a nonlinear, nonparametric classification method, can also provide a powerful framework for comparing, assessing, and combining information from different expert systems, by treating their predictions as the independent variables in a classification tree analysis. This paper discusses the applied problem of classifying chemicals as human carcinogens. It shows how classification trees can be used to compare the information provided by ten different carcinogen classification expert systems, construct an improved "hybrid" classification system from them, and identify cost-effective combinations of assays (the inputs to the expert systems) to use in classifying chemicals in future.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR2-cox99a, title = {Causal Mechanisms and Classification Trees for Predicting Chemical Carcinogens}, author = {Cox, Jr, Louis Anthony}, booktitle = {Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics}, pages = {18--26}, year = {1999}, editor = {Heckerman, David and Whittaker, Joe}, volume = {R2}, series = {Proceedings of Machine Learning Research}, month = {03--06 Jan}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/r2/cox99a/cox99a.pdf}, url = {https://proceedings.mlr.press/r2/cox99a.html}, abstract = {Classification trees, usually used as a nonlinear, nonparametric classification method, can also provide a powerful framework for comparing, assessing, and combining information from different expert systems, by treating their predictions as the independent variables in a classification tree analysis. This paper discusses the applied problem of classifying chemicals as human carcinogens. It shows how classification trees can be used to compare the information provided by ten different carcinogen classification expert systems, construct an improved "hybrid" classification system from them, and identify cost-effective combinations of assays (the inputs to the expert systems) to use in classifying chemicals in future.}, note = {Reissued by PMLR on 20 August 2020.} }
Endnote
%0 Conference Paper %T Causal Mechanisms and Classification Trees for Predicting Chemical Carcinogens %A Louis Anthony Cox, Jr %B Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 1999 %E David Heckerman %E Joe Whittaker %F pmlr-vR2-cox99a %I PMLR %P 18--26 %U https://proceedings.mlr.press/r2/cox99a.html %V R2 %X Classification trees, usually used as a nonlinear, nonparametric classification method, can also provide a powerful framework for comparing, assessing, and combining information from different expert systems, by treating their predictions as the independent variables in a classification tree analysis. This paper discusses the applied problem of classifying chemicals as human carcinogens. It shows how classification trees can be used to compare the information provided by ten different carcinogen classification expert systems, construct an improved "hybrid" classification system from them, and identify cost-effective combinations of assays (the inputs to the expert systems) to use in classifying chemicals in future. %Z Reissued by PMLR on 20 August 2020.
APA
Cox, Jr, L.A.. (1999). Causal Mechanisms and Classification Trees for Predicting Chemical Carcinogens. Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research R2:18-26 Available from https://proceedings.mlr.press/r2/cox99a.html. Reissued by PMLR on 20 August 2020.

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