2023
DOI: 10.3390/pr11082409
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Feature Selection of Microarray Data Using Simulated Kalman Filter with Mutation

Abstract: Microarrays have been proven to be beneficial for understanding the genetics of disease. They are used to assess many different types of cancers. Machine learning algorithms, like the artificial neural network (ANN), can be trained to determine whether a microarray sample is cancerous or not. The classification is performed using the features of DNA microarray data, which are composed of thousands of gene values. However, most of the gene values have been proven to be uninformative and redundant. Meanwhile, th… Show more

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Cited by 5 publications
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“…Our R function can be altered for other machine learning methods as well. We use SVM since in the literature, it is a commonly used technique when evaluating the performance of the feature selection approach for microarray data in cancer classification problems [ 34 37 ]. The parameters of the SVM with the radial basis function (RBF) kernel are optimized using the tune.svm function in R. We briefly introduce the methods used in the steps of the SNFS in the following subsections.…”
Section: Methodsmentioning
confidence: 99%
“…Our R function can be altered for other machine learning methods as well. We use SVM since in the literature, it is a commonly used technique when evaluating the performance of the feature selection approach for microarray data in cancer classification problems [ 34 37 ]. The parameters of the SVM with the radial basis function (RBF) kernel are optimized using the tune.svm function in R. We briefly introduce the methods used in the steps of the SNFS in the following subsections.…”
Section: Methodsmentioning
confidence: 99%