Model Validation: A statistical-based criterium of hypotheses acceptance in numerical reasoning
Model Validation: A statistical-based criterium of hypotheses acceptance in numerical reasoning
Current ILP systems that perform numerical reasoning, select the best hypothesis using exclusively the scored value of the cost function. The cost function, by itself, cannot guarantee the goodness-offit of the induced hypotheses in numerical domains. Consequently the induced theory may not capture the overall structure of the underlying process that generated data. This paper proposes a statistical-based criterion for hypotheses acceptance, called model validation, that assess the goodness-of-fit of the induced hypotheses in numerical domains. We have found this extension essential to improve on results over ML and statistical-based algorithms used in the empirical evaluation study.
Couto Alves, Alexessander
87b9179e-abde-4ca5-abfc-4b7c5ac8b03b
Camacho, Rui
8ff65c85-a4e7-4278-ad6c-e27a2d469416
Oliveira, Eugenio E.
917959d1-5292-4036-a34b-0489d3238ad9
6 September 2004
Couto Alves, Alexessander
87b9179e-abde-4ca5-abfc-4b7c5ac8b03b
Camacho, Rui
8ff65c85-a4e7-4278-ad6c-e27a2d469416
Oliveira, Eugenio E.
917959d1-5292-4036-a34b-0489d3238ad9
Couto Alves, Alexessander, Camacho, Rui and Oliveira, Eugenio E.
(2004)
Model Validation: A statistical-based criterium of hypotheses acceptance in numerical reasoning.
In Proc. 14th Int. Conf. Inductive Logic Program.
6 pp
.
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Conference or Workshop Item
(Paper)
Abstract
Current ILP systems that perform numerical reasoning, select the best hypothesis using exclusively the scored value of the cost function. The cost function, by itself, cannot guarantee the goodness-offit of the induced hypotheses in numerical domains. Consequently the induced theory may not capture the overall structure of the underlying process that generated data. This paper proposes a statistical-based criterion for hypotheses acceptance, called model validation, that assess the goodness-of-fit of the induced hypotheses in numerical domains. We have found this extension essential to improve on results over ML and statistical-based algorithms used in the empirical evaluation study.
More information
Published date: 6 September 2004
Venue - Dates:
14th International Conference, Inductive Logic Programmig ILP 2004, Porto, Portugal, September 6-8, 2004, Procee, Porto, Porto, Portugal, 2004-09-06 - 2004-09-08
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Local EPrints ID: 494915
URI: http://eprints.soton.ac.uk/id/eprint/494915
PURE UUID: 8932451c-6f90-4dc3-9f41-5fbbde7198ee
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Date deposited: 23 Oct 2024 16:33
Last modified: 24 Oct 2024 02:11
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Contributors
Author:
Alexessander Couto Alves
Author:
Rui Camacho
Author:
Eugenio E. Oliveira
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