We are analyzing https://link.springer.com/article/10.1186/1471-2105-9-319.

Title:
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification | BMC Bioinformatics
Description:
Background Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. Results In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. Conclusion We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.
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Keywords {🔍}

classification, gene, data, performance, datasets, microarray, random, selection, svms, google, scholar, genes, rfs, article, number, expression, comparison, forests, methods, support, cancer, tasks, svm, vector, classifiers, dataset, parameters, average, table, classifier, machines, outperform, decision, method, algorithms, learning, multicategory, algorithm, applied, training, crossvalidation, bioinformatics, work, prior, trees, error, size, model, information, large,

Topics {✒️}

open access article microarray-based cancer classification rf-related analysis protocols article download pdf multi-class feature selection microarray gene expression paired-difference algorithm comparisons training-set-size bias mtry parameter denoting microarray expression data published microarray studies machine learning benchmarks gene expression data support vector machines high-throughput diagnostic test gene expression monitoring input parameters mtry perform backward elimination backward elimination procedure privacy choices/manage cookies performs backward elimination sound research design obtained cross-validation performance significantly outperform svms necessarily statistically significantly gene expression values classification algorithms compared approach involves building random forest model larger gene sets colon cancer based research design limitations full size image random forest classifiers complex classification functions random forest regression authors’ original file automated cancer diagnosis clinical decision support linear decision functions related subjects microarrays entropy-based measure tw/~cjlin/libsvm siam international conference linearly separable functions author information authors performance statistically indistinguishable accurate classification algorithms classification performance metrics

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         headline:A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
         description:Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.
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      headline:A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification
      description:Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.
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         Computer Appl. in Life Sciences
         Algorithms
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