Aliferis C F, Tsamardinos I, Statnikov A
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
AMIA Annu Symp Proc. 2003;2003:21-5.
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.
(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.
我们介绍了一种用于分类、回归和预测的变量选择的新颖、合理、样本高效且高度可扩展的算法，称为HITON。该算法通过诱导要分类或预测的变量的马尔可夫毯来工作。使用了具有不同特征的各种生物医学任务进行实证评估。具体而言，（i）药物发现的生物活性预测，（ii）心律失常的临床诊断，（iii）文献文本分类，（iv）从基因表达阵列数据进行肺癌诊断，以及（v）基于蛋白质组学的前列腺癌检测。为进行基线比较，为每个领域选择了最先进的算法。
（1）相对于原始变量集，HITON在预测模型中减少了三个数量级的变量数量，同时提高或保持了准确性。（2）在选定的任务和数据集中，HITON通过选择比基线小两个数量级以上的变量集，优于基线算法。
