CANCER CLASSIFICATION BASED ON MICROARRAY DATA USING BINARY-TUBE TREE
Keywords:
Classification, microarray data, binary-tube tree, decision treeAbstract
Analysis of large gene expression data via machine learning has been widely acknowledged to accurately predict cancer classes from the microarray data which are cast as the numerical matrix from a collection of complex RNAs. This research proposes a novel continuous value binary-tube tree constructed from the core vector of multi-features and the tolerance radius of the specific cancer types. To validate this classifier, the accuracy measure and Matthews correlation coefficient measure are compared with the support vector machine and the decision tree classifier using k-folds cross-validation. The result shows that the binary-tube tree outperforms the support vector machine and the decision tree classifier for leukemia, colon cancer, lymphoma, and breast cancer; only the lung cancer data set shows no significant improvement.
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