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Gene-Expression Based Cancer Classification From Microarray Data: Through Statistical Feature Selection - Brossura
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Microarray technology is used for monitoring thousands of genes at a similar time. This work employs feature selection technique to identify the differently expressed genes by selecting a subset of genes, selecting top ranked genes or removing the redundant genes for better classification model. This work presents the efficiency of three feature selection methods namely one-way ANOVA, Kruskall-Wallis and T-Test for gene selection on three publically available microarray dataset followed by classification of those using Naive Bayes, Binary SVM and Multiclass SVM classification algorithms. The results show the effectiveness of feature selection algorithms on three microarray cancer datasets namely MLL_Leukemia, Lung and SRBCT.
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Gene-Expression Based Cancer Classification From Microarray Data
Editore:
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200434131
ISBN 13: 9786200434135
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Brossura
Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: K NirmalakumariK. Nirmalakumari is an Assistant Professor (Level III), Department of ECE, Bannari Amman Institute of Technology, Sathyamangalam.Microarray technology is used for monitoring thousands of genes at a similar time. Th. Codice articolo 335816202
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Gene-Expression Based Cancer Classification From Microarray Data
Editore:
LAP LAMBERT Academic Publishing Okt 2019, 2019
ISBN 10: 6200434131
ISBN 13: 9786200434135
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Taschenbuch
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Microarray technology is used for monitoring thousands of genes at a similar time. This work employs feature selection technique to identify the differently expressed genes by selecting a subset of genes, selecting top ranked genes or removing the redundant genes for better classification model. This work presents the efficiency of three feature selection methods namely one-way ANOVA, Kruskall-Wallis and T-Test for gene selection on three publically available microarray dataset followed by classification of those using Naive Bayes, Binary SVM and Multiclass SVM classification algorithms. The results show the effectiveness of feature selection algorithms on three microarray cancer datasets namely MLL_Leukemia, Lung and SRBCT. 56 pp. Englisch. Codice articolo 9786200434135
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Gene-Expression Based Cancer Classification From Microarray Data
Editore:
LAP LAMBERT Academic Publishing Okt 2019, 2019
ISBN 10: 6200434131
ISBN 13: 9786200434135
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Taschenbuch
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. Neuware -Microarray technology is used for monitoring thousands of genes at a similar time. This work employs feature selection technique to identify the differently expressed genes by selecting a subset of genes, selecting top ranked genes or removing the redundant genes for better classification model. This work presents the efficiency of three feature selection methods namely one-way ANOVA, Kruskall-Wallis and T-Test for gene selection on three publically available microarray dataset followed by classification of those using Naive Bayes, Binary SVM and Multiclass SVM classification algorithms. The results show the effectiveness of feature selection algorithms on three microarray cancer datasets namely MLL_Leukemia, Lung and SRBCT.Books on Demand GmbH, Überseering 33, 22297 Hamburg 56 pp. Englisch. Codice articolo 9786200434135
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Gene-Expression Based Cancer Classification From Microarray Data : Through Statistical Feature Selection
Editore:
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200434131
ISBN 13: 9786200434135
Nuovo
Taschenbuch
Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Microarray technology is used for monitoring thousands of genes at a similar time. This work employs feature selection technique to identify the differently expressed genes by selecting a subset of genes, selecting top ranked genes or removing the redundant genes for better classification model. This work presents the efficiency of three feature selection methods namely one-way ANOVA, Kruskall-Wallis and T-Test for gene selection on three publically available microarray dataset followed by classification of those using Naive Bayes, Binary SVM and Multiclass SVM classification algorithms. The results show the effectiveness of feature selection algorithms on three microarray cancer datasets namely MLL_Leukemia, Lung and SRBCT. Codice articolo 9786200434135
Quantità: 1 disponibili