Multivariate rule building expert system

Harrington, Peter de B.; Voorhees, Kent J.

doi:10.1021/ac00206a016

Cited by 44 publications

(15 citation statements)

References 13 publications

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“…Medical applications such as lymphography, prognosis of breast cancer recurrence, location of primary tumour and thyroid problem diagnosis have been reported [5,24,25]. Other applications include investment appraisal [26], forensic classification of glass fragment evidence [27], extraction of decision rules for analysis of test data for the space shuttle main engine [28], experimental generation of decision rules for the conceptual design of steel members under bending [10], soil classification [29], stock control [30], software resource analysis [31], assessing credit card applications [32], military decision making [33], dynamic system identification [34,35], engine fault diagnosis [36] and identification of the mass-spectra of complex materials [37,38,39].…”

Section: Applications Of Inductive Learningmentioning

confidence: 99%

A Review of Rules Family of Algorithms

Aksoy

2008

MCA

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In recent years, there has been a growing amount of research on inductive learning. Out of this research a number of promising algorithms have surfaced. In the paper after a brief description of knowledge acquisition, induction and inductive learning; RULES family of inductive learning algorithms, their strengths as well as weaknesses are explained and discussed. The applications of inductive learning and particularly the applications of RULES family of algorithms are overviewed.

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Section: Applications Of Inductive Learningmentioning

confidence: 99%

A Review of Rules Family of Algorithms

Aksoy

2008

MCA

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“…Multivariate analysis was performed with the Resolve Software Package developed at the Colorado School of Mines [28]. Mass spectra were collected as a set of raw intensities and normalized to constant length to correct for variations in total ion current.…”

Section: Multivariate Analysismentioning

confidence: 99%

Rapid detection of taxonomically important fatty acid methyl ester and steroid biomarkers using in situ thermal hydrolysis/methylation mass spectrometry (THM-MS): implications for bioaerosol detection

Madonna

Voorhees

Hadfield

2001

Journal of Analytical and Applied Pyrolysis

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Implications for the rapid interrogation of biological materials collected from the atmosphere using a simple, one step, sample preparation technique was explored. For this purpose, various samples of whole bacteria, fungi, pollen, media contaminated with viruses, and proteins were treated with an aliquot of methanolic tetramethylammonium hydroxide prior to thermal introduction into the ion source of a triple quadrupole mass spectrometer. Molecular and fragment ions, consistent with fatty acid methyl esters (FAMEs) and steroids (non-methylated and methylated), generated during electron ionization (70 eV) of the volatile hydrolysates were subsequently detected. The varying distributions and relative intensities of these ions were used to discriminate between the different biological samples. More specifically, it was found that polyunsaturated FAMEs and steroids could be used to differentiate eukaryotic cells from prokaryotic cells since the latter do not generally synthesize either of these lipid membrane constituents. Further discrimination of the different eukaryotic samples was made based on the detection of ergosterol for fungi, cholesterol for the viral media, and C18:3Me for pollen. Multivariate statistical analysis was employed to evaluate and compare the large set of mass spectra generated during the study and to build a trained model for predicting the class membership of test samples entered as unknowns. Of 132 different samples subjected to the model as unknowns, 131 were correctly classified into their proper biological categories. Moreover, 29 out of 30 bacteria test samples representing five species : S 0 1 6 5 -2 3 7 0 ( 0 1 ) 0 0 1 3 6 -X 66 A.J. Madonna et al. / J. Anal. Appl. Pyrolysis 61 (2001) 65-89 of pathogenic bacteria were correctly classified at the species level.

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“…For leaf nodes of the same class designation the certainty factors are added together, which provides AND/OR linkage of the rules. The certainty factors of observations used for training will always sum to unity because of the properties inherent in the logistic functions (4) and (5). Classifications for which the certainty factors will all be the same are obtained for observations that were not characterized by the training set.…”

Section: Theorymentioning

confidence: 99%

Fuzzy multivariate rule‐building expert systems: Minimal neural networks

Harrington

1991

Journal of Chemometrics

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SUMMARYA fuzzy multivariate rule-building expert system (FuRES) has been devised which also functions as a minimal neural network. This system builds rules from training sets of data that use feature transformation in their antecedents. The rules are constructed using the ID3 algorithm with a fuzzy expression of classification entropy. The rules are optimal with respect to fuzziness and can accommodate overlapped and underlapped clusters of data. The FuRES algorithm combines the benefits obtained from simulated annealing and gradient optimization, which provide robustness and efficiency respectively. FuRES classification trees support OR logic in their inference. The system automatically generates meaningful and consistent certainty factors during rule construction. Unlike other neural networks, FuRES uses local processing which furnishes qualitative information in the rule structure of its classification trees and variable loadings of the weight vectors.

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Multivariate rule building expert system

Cited by 44 publications

References 13 publications

A Review of Rules Family of Algorithms

A Review of Rules Family of Algorithms

Rapid detection of taxonomically important fatty acid methyl ester and steroid biomarkers using in situ thermal hydrolysis/methylation mass spectrometry (THM-MS): implications for bioaerosol detection

Fuzzy multivariate rule‐building expert systems: Minimal neural networks

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