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  A new Classification and prediction model with Twostage Gene selection method using minimal subsets of Gene Expression data
 
 
Titel: A new Classification and prediction model with Twostage Gene selection method using minimal subsets of Gene Expression data
Auteur: Mallika R.
Saravanan V.
Verschenen in: International journal of machine intelligence
Paginering: Jaargang 1 (2009) nr. 2 pagina's 14-25
Jaar: 2009
Inhoud: Data mining models are extensively used in the field of diseasediagnosis. Gene expression data are a main factor for the success of diseasediagnosis. With thousands of gene expression data, gene selection is being a bigchallenge prior to classification. The proposed method incorporates two stages ingene selection. In the first stage pair wise gene selection was performed using apopular statistical technique. In the second stage the gene pairs that achieved100% Cross Validation (CV) accuracy of those genes selected in first stage wereused for classification. The testing results were compared with the single stagemethod and improvement on the computational burden was also proven to be thebest in the proposed two-stage method. The paper also compares theperformances of the three different classifiers Support Vector Machines (SVM), KNearest Neighbour (KNN), Linear Discriminant Analysis (LDA) and promisingresults have been achieved.
Uitgever: Bioinfo Publications (provided by DOAJ)
Bronbestand: Elektronische Wetenschappelijke Tijdschriften
 
 

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