HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection
HITON: A Novel Markov Blanket Algorithm for Optimal Variable Selection
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发表时间：
2003
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通讯作者：
C. Aliferis;I. Tsamardinos;A. Statnikov
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作者：
C. Aliferis;I. Tsamardinos;A. Statnikov
UNLABELLED 我们引入了一种新颖、可靠、样本高效且高度可扩展的算法，用于分类、回归和预测的变量选择，称为 HITON。该算法的工作原理是引入要分类或预测的变量的马尔可夫毯。使用具有不同特征的各种生物医学任务进行实证评估。即，（i）药物发现的生物活性预测，（ii）心律失常的临床诊断，（iii）书目文本分类，（iv）从基因表达阵列数据诊断肺癌，以及（v）基于蛋白质组学的前列腺癌检测。选择每个领域最先进的算法进行基线比较。结果 (1) HITON 将预测模型中的变量数量相对于原始变量集减少了三个数量级，同时提高或保持了准确性。 (2) HITON 在选定的任务和数据集中选择比基线小两个数量级以上的变量集，从而优于基线算法。
UNLABELLED We introduce a novel, sound, sample-efficient, and highly-scalable algorithm for variable selection for classification, regression and prediction called HITON. The algorithm works by inducing the Markov Blanket of the variable to be classified or predicted. A wide variety of biomedical tasks with different characteristics were used for an empirical evaluation. Namely, (i) bioactivity prediction for drug discovery, (ii) clinical diagnosis of arrhythmias, (iii) bibliographic text categorization, (iv) lung cancer diagnosis from gene expression array data, and (v) proteomics-based prostate cancer detection. State-of-the-art algorithms for each domain were selected for baseline comparison. RESULTS (1) HITON reduces the number of variables in the prediction models by three orders of magnitude relative to the original variable set while improving or maintaining accuracy. (2) HITON outperforms the baseline algorithms by selecting more than two orders-of-magnitude smaller variable sets than the baselines, in the selected tasks and datasets.