Using inductive logic programming to discover knowledge hidden in chemical data
Bryant, CH, Adam, AE, Taylor, DR and Rowe, RC 1997, 'Using inductive logic programming to discover knowledge hidden in chemical data' , Chemometrics and Intelligent Laboratory Systems, 36 (2) , pp. 111-123.
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This paper demonstrates how general purpose tools from the field of Inductive Logic Programming (ILP) can be applied to analytical chemistry. As far as these authors are aware, this is the first published work to describe the application of the ILP tool Golem to separation science. An outline of the theory of ILP is given, together with a description of Golem and previous applications of ILP. The advantages of ILP over classical machine induction techniques, such as the Top-Down-Induction-of-Decision-Tree family, are explained. A case-study is then presented in which Golem is used to induce rules which predict, with a high accuracy (82%), whether each of a series of attempted separations succeed or fail. The separation data was obtained from published work on the attempted separation of a series of 3-substituted phthalide enantiomer pairs on (R)-N-(3,5-dinitrobenzoyl)-phenylglycine.
|Uncontrolled Keywords:||inductive logic programming, golem|
|Themes:||Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science|
Subjects / Themes > Q Science > QD Chemistry
Subjects outside of the University Themes
|Schools:||Colleges and Schools > College of Science & Technology|
Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering
Colleges and Schools > College of Science & Technology > School of Computing, Science and Engineering > Data Mining and Pattern Recognition Research Centre
|Journal or Publication Title:||Chemometrics and Intelligent Laboratory Systems|
|Depositing User:||Dr Chris H. Bryant|
|Date Deposited:||17 Feb 2009 14:56|
|Last Modified:||20 Aug 2013 16:56|
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