An improved genetic programming technique for the classification of Raman spectra

Kenneth Hennessy; Michael G. Madden; Jennifer Conroy; Alan G. Ryder

doi:10.1016/j.knosys.2004.10.001

An improved genetic programming technique for the classification of Raman spectra

Kenneth Hennessy
, Michael G. Madden
, Jennifer Conroy
, Alan G. Ryder

University of Galway

Research output: Contribution to a Journal (Peer & Non Peer) › Article › peer-review

19 Citations (Scopus)

Abstract

The aim of this study is to evaluate the effectiveness of genetic programming relative to that of more commonly-used methods for the identification of components within mixtures of materials using Raman spectroscopy. A key contribution of the genetic programming technique proposed in this research is that it explicitly aims to optimise the certainty levels associated with discovered rules, so as to minimize the chance of misclassification of future samples.

Original language	English
Pages (from-to)	217-224
Number of pages	8
Journal	Knowledge-Based Systems
Volume	18
Issue number	4-5
DOIs	https://doi.org/10.1016/j.knosys.2004.10.001
Publication status	Published - Aug 2005

Keywords

Genetic programming
Machine learning
Neural networks
Raman
Spectroscopy

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10.1016/j.knosys.2004.10.001

Cite this

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title = "An improved genetic programming technique for the classification of Raman spectra",

abstract = "The aim of this study is to evaluate the effectiveness of genetic programming relative to that of more commonly-used methods for the identification of components within mixtures of materials using Raman spectroscopy. A key contribution of the genetic programming technique proposed in this research is that it explicitly aims to optimise the certainty levels associated with discovered rules, so as to minimize the chance of misclassification of future samples.",

keywords = "Genetic programming, Machine learning, Neural networks, Raman, Spectroscopy",

author = "Kenneth Hennessy and Madden, \{Michael G.\} and Jennifer Conroy and Ryder, \{Alan G.\}",

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AU - Hennessy, Kenneth

AU - Madden, Michael G.

AU - Conroy, Jennifer

AU - Ryder, Alan G.

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AB - The aim of this study is to evaluate the effectiveness of genetic programming relative to that of more commonly-used methods for the identification of components within mixtures of materials using Raman spectroscopy. A key contribution of the genetic programming technique proposed in this research is that it explicitly aims to optimise the certainty levels associated with discovered rules, so as to minimize the chance of misclassification of future samples.

KW - Genetic programming

KW - Machine learning

KW - Neural networks

KW - Raman

KW - Spectroscopy

UR - https://www.scopus.com/pages/publications/22544457943

U2 - 10.1016/j.knosys.2004.10.001

DO - 10.1016/j.knosys.2004.10.001

M3 - Article

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SN - 0950-7051

VL - 18

SP - 217

EP - 224

JO - Knowledge-Based Systems

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IS - 4-5

ER -

An improved genetic programming technique for the classification of Raman spectra

Abstract

Keywords

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