2022
DOI: 10.1186/s13040-022-00304-y
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Effective hybrid feature selection using different bootstrap enhances cancers classification performance

Abstract: Background Machine learning can be used to predict the different onset of human cancers. Highly dimensional data have enormous, complicated problems. One of these is an excessive number of genes plus over-fitting, fitting time, and classification accuracy. Recursive Feature Elimination (RFE) is a wrapper method for selecting the best subset of features that cause the best accuracy. Despite the high performance of RFE, time computation and over-fitting are two disadvantages of this algorithm. Ra… Show more

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Cited by 4 publications
(2 citation statements)
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“…Bootstrap is a widely used statistical tool that is very powerful in quantifying the uncertainty associated with a given estimator or statistical learning method. As a simple example, Bootstrap can be used to estimate the standard errors of linear regression coefficients even though standard statistical software such as R can automatically output the standard errors [11][12][13][14].…”
Section: Methodsmentioning
confidence: 99%
“…Bootstrap is a widely used statistical tool that is very powerful in quantifying the uncertainty associated with a given estimator or statistical learning method. As a simple example, Bootstrap can be used to estimate the standard errors of linear regression coefficients even though standard statistical software such as R can automatically output the standard errors [11][12][13][14].…”
Section: Methodsmentioning
confidence: 99%
“…There is also research related to the selection of hybrid characteristics to improve the performance of cancer classification, for this he uses different bootstrap which is a representative sample of resampling with replacement before the selection step, in his research he concludes that the sets high-dimensional data and the recursive function elimination (RFE) algorithm face many problems in cancer classification performance. To do this, he proposes positioning the first step of bootstrap (PFBS), random forest for selection (RFS), recursive function elimination (RFE) that will solve problems with different positions [18], as well as other research related -to health, especially with skin. It is the diseases generated by melanoma and carcinoma are usually quite difficult to detect, one of the types of skin cancer is caused by melanoma, in this investigation a method is established to identify if a certain sample is affected by melanoma, for this purpose, collects data from labeled preprocessed images, the results of the samples have an accuracy of 90% of classification [19].…”
Section: Literature Reviewmentioning
confidence: 99%