Gene-Expression Based Cancer Classification From Microarray Data: Through Statistical Feature Selection - Nirmalakumari K,Ganesh Babu C.,Harikumar Rajaguru
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Piegāde 15-21 darba dienu laikā
30 dienu atgriešanas politika
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- ... Pilns apraksts
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Aprašymas
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.
Vairāk informācijas
| Autors | Nirmalakumari K, Ganesh Babu C., Harikumar Rajaguru |
|---|---|
| Izdevējs | LAP LAMBERT Academic Publishing |
| Izlaides gads | 2019 |
| Vāka tips | Mīkstais vāks |
| EAN | 9786200434135 |