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• W2068707911 endingPage "CIN.S7111" @default.
• W2068707911 startingPage "CIN.S7111" @default.
• W2068707911 abstract "With technological advances now allowing measurement of thousands of genes, proteins and metabolites, researchers are using this information to develop diagnostic and prognostic tests and discern the biological pathways underlying diseases. Often, an investigator's objective is to develop a classification rule to predict group membership of unknown samples based on a small set of features and that could ultimately be used in a clinical setting. While common classification methods such as random forest and support vector machines are effective at separating groups, they do not directly translate into a clinically-applicable classification rule based on a small number of features.We present a simple feature selection and classification method for biomarker detection that is intuitively understandable and can be directly extended for application to a clinical setting. We first use a jackknife procedure to identify important features and then, for classification, we use voting classifiers which are simple and easy to implement. We compared our method to random forest and support vector machines using three benchmark cancer 'omics datasets with different characteristics. We found our jackknife procedure and voting classifier to perform comparably to these two methods in terms of accuracy. Further, the jackknife procedure yielded stable feature sets. Voting classifiers in combination with a robust feature selection method such as our jackknife procedure offer an effective, simple and intuitive approach to feature selection and classification with a clear extension to clinical applications." @default.
• W2068707911 created "2016-06-24" @default.
• W2068707911 creator A5049236790 @default.
• W2068707911 creator A5083833302 @default.
• W2068707911 date "2011-01-01" @default.
• W2068707911 modified "2023-09-30" @default.
• W2068707911 title "A Jackknife and Voting Classifier Approach to Feature Selection and Classification" @default.
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• W2068707911 doi "https://doi.org/10.4137/cin.s7111" @default.
• W2068707911 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3091410" @default.
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• W2068707911 hasPublicationYear "2011" @default.
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