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Table 1 Accuracy of six classifiers

From: Tissue-based Alzheimer gene expression markers–comparison of multiple machine learning approaches and investigation of redundancy in small biomarker sets

 

AD

PLURI

Naive Bayes

81.4%

87.1%

C4.5 decision tree

78.9%

95.1%

Nearest neighbor

87.0%

96.5%

Random Forest

87.0%

97.2%

SVM + Gaussian kernel

85.7%

97.9%

SVM + linear kernel

91.9%

99.0%

  1. The classification results of different methods on the two 1,000 gene data sets AD and PLURI. The classification accuracy is computed as average from a 3-fold cross-validation.
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BMC Bioinformatics

ISSN: 1471-2105

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