Gene-Expression Based Cancer Classification From Microarray Data: Through Statistical Feature Selection - Nirmalakumari K,Ganesh Babu C.,Harikumar Rajaguru
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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- ... Visas aprašymas
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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.
Daugiau informacijos
| Autorius | Nirmalakumari K, Ganesh Babu C., Harikumar Rajaguru |
|---|---|
| Leidėjas | LAP LAMBERT Academic Publishing |
| Išleidimo metai | 2019 |
| Viršelio tipas | Minkšti viršeliai |
| EAN | 9786200434135 |