Genetic algorithm for fuel spill identification

Lavine, Barry K.; Brzozowski, D; Moores, Anthony J.; Davidson, Charles E.; Mayfield, Howard T.

doi:10.1016/s0003-2670(01)00946-1

Cited by 42 publications

(16 citation statements)

References 10 publications

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“…Rather than identifying peaks by retention time, one could use relative retention times or retention indices in order to adjust for slight variations in the retention times of peaks. Algorithms for aligning peak tables exist and perform well, so long as some peaks can be easily and reliably matched across all chromatograms (Lavine et al, 2001). …”

Section: Peak Tablesmentioning

confidence: 99%

Application of Chemometrics to the Interpretation of Analytical Separations Data

Harynuk¹,

Mata²,

Sinkov³

2012

Chemometrics in Practical Applications

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Section: Peak Tablesmentioning

confidence: 99%

Application of Chemometrics to the Interpretation of Analytical Separations Data

Harynuk¹,

Mata²,

Sinkov³

2012

Chemometrics in Practical Applications

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“…Class and sample weights are adjusted during each generation through a process called boosting (which is discussed in the next section entitled adjustment of internal parameters). Further details about PCKaNN can be found elsewhere [16][17][18][19][20] …”

Section: Fitness Functionmentioning

confidence: 99%

One stop shopping: feature selection, classification and prediction in a single step

Lavine

Nuguru

Mirjankar

2011

Journal of Chemometrics

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We report on the application of a genetic algorithm (GA) for pattern recognition that uses both supervised and transverse learning to mine spectroscopic and proteomic data. The pattern recognition GA selects features that optimize the separation of the classes in a plot of the two or three largest principal components of the data. For training sets with small amounts of labeled data (i.e. data points tagged with a class label) and large amounts of unlabeled data (i.e. data points that are not tagged with a class label), this approach is preferred, as our results show, information in the unlabeled data is used by the fitness function to guide feature selection. The advantages of incorporating transverse learning into the fitness function of the pattern recognition GA have been evaluated in two recently published studies by our group. In one study, Raman spectroscopy and the pattern recognition GA were used to develop a potential method to discriminate hardwoods, softwoods and tropical woods. In a second study, biopsy material of small round blue cell tumors analyzed by cDNA microarrays was identified as to type (Ewings sarcoma, Burkitt's lymphoma, neuroblastoma and rhabdomyosarcoma) through supervised learning implemented by the pattern recognition GA.

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“…For example, some of the concepts that have been described in a four part series on the effects of refining on finished petroleum products may find an application in fire investigation [298][299][300][301]. In the area of pattern recognition analysis, extensive work has been performed on the fingerprinting of spills of heavier fuels such as jet fuel spills [302,303]. This research group has also explored the use of SPME and pattern recognition techniques to classify jet fuels by type [304].…”

Section: Chemical Fingerprintingmentioning

confidence: 99%

Fire investigation and ignitable liquid residue analysis—A review: 2001–2007

Sandercock

2008

Forensic Science International

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Genetic algorithm for fuel spill identification

Cited by 42 publications

References 10 publications

Application of Chemometrics to the Interpretation of Analytical Separations Data

Application of Chemometrics to the Interpretation of Analytical Separations Data

One stop shopping: feature selection, classification and prediction in a single step

Fire investigation and ignitable liquid residue analysis—A review: 2001–2007

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