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Journal Article (Refereed)
January 1997

Using Inductive Logic Programming to Discover Knowledge Hidden in Chemical Data

Bryant, C & Adam, A E & Taylor, D & Rowe, R 1997, 'Using Inductive Logic Programming to Discover Knowledge Hidden in Chemical Data', Chemometrics and Intelligent Laboratory Systems, 36(2), pp.111-123.

Abstract

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.

Notes

ISSN = 0169-7439

Authors

SEEK Members

External Authors

D.R. Taylor

R.C. Rowe

Publication Details

Journal Name
Chemometrics and Intelligent Laboratory Systems

Volume
36(2)

Pagination
111-123.