Discovery of functional relationships in multi-relational data using inductive logic programming
Discovery of functional relationships in multi-relational data using inductive logic programming
ILP systems have been largely applied to data mining classification tasks with a considerable success. The use of ILP systems in regression tasks has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the application of ILP to discovery of functional relationships of numeric nature. This paper proposes improvements in numerical reasoning capabilities of ILP systems for dealing with regression tasks. It proposes the use of statistical-based techniques like model validation and model selection to improve noise handling and it introduces a search stopping criterium based on the PAC method to evaluate learning performance. We have found these extensions essential to improve on results over machine learning and statistical-based algorithms used in the empirical evaluation study.
319-322
Couto Alves, Alexessander
87b9179e-abde-4ca5-abfc-4b7c5ac8b03b
Camacho, Rui
8ff65c85-a4e7-4278-ad6c-e27a2d469416
Oliveira, Eugenio E.
917959d1-5292-4036-a34b-0489d3238ad9
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)
Discovery of functional relationships in multi-relational data using inductive logic programming.
In Fourth IEEE International Conference on Data Mining (ICDM'04).
IEEE.
.
(doi:10.1109/ICDM.2004.10053).
Record type:
Conference or Workshop Item
(Paper)
Abstract
ILP systems have been largely applied to data mining classification tasks with a considerable success. The use of ILP systems in regression tasks has been far less successful. Current systems have very limited numerical reasoning capabilities, which limits the application of ILP to discovery of functional relationships of numeric nature. This paper proposes improvements in numerical reasoning capabilities of ILP systems for dealing with regression tasks. It proposes the use of statistical-based techniques like model validation and model selection to improve noise handling and it introduces a search stopping criterium based on the PAC method to evaluate learning performance. We have found these extensions essential to improve on results over machine learning and statistical-based algorithms used in the empirical evaluation study.
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Published date: 2004
Venue - Dates:
Fourth IEEE International Conference on Data Mining, , Brighton, United Kingdom, 2004-11-01 - 2004-11-04
Identifiers
Local EPrints ID: 494633
URI: http://eprints.soton.ac.uk/id/eprint/494633
PURE UUID: 3fe8029a-fef2-4e8e-8312-36e35799cecb
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Date deposited: 11 Oct 2024 16:50
Last modified: 12 Oct 2024 03:06
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Contributors
Author:
Alexessander Couto Alves
Author:
Rui Camacho
Author:
Eugenio E. Oliveira
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