Gene-Expression Based Cancer Classification From Microarray Data

Through Statistical Feature Selection
Autor: Nirmalakumari K
CHF 54.40
ISBN: 978-620-0-43413-5
Einband: Kartonierter Einband (Kt)
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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.

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.

Autor Nirmalakumari K
Verlag LAP Lambert Academic Publishing
Einband Kartonierter Einband (Kt)
Erscheinungsjahr 2019
Seitenangabe 56 S.
Ausgabekennzeichen Englisch
Abbildungen Paperback
Masse H22.0 cm x B15.0 cm x D0.4 cm 102 g

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