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首页> 外文期刊>Journal of Computers >ORPSW: a new classifier for gene expression data based on optimal risk and preventive patterns
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ORPSW: a new classifier for gene expression data based on optimal risk and preventive patterns

机译：ORPSW：基于最佳风险和预防模式的基因表达数据的新分类器

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—Optimal risk and preventive patterns are itemsets which can identify characteristics of cohorts of individuals who have significantly disproportionate representation in the abnormal and normal groups. In this paper, we propose a new classifier namely ORPSW (Optimal Risk and Preventive Sets with Weights) to classify gene expression data based on optimal risk and preventive patterns. The proposed method has been tested on four bench-mark gene expression data sets to compare with three state-of-the-art classifiers: C4.5, Naive Bayes and SVM. The experiments show that ORPSW classifier is more accurate than C4.5 and Naive Bayes classifiers in general, and is comparable with SVM classifier. Observing that accuracy is sensitive to the prior distribution of the class, we also used false positive rate (FPR) and false negative rate (FNR), to better characterize the performance of classifiers. ORPSW classifier is also very good under this measure. It provides differentially expressed genes in different classes, which help better understand classification process.
机译：- 优化的风险和预防模式是项目集，可以识别在异常和正常组中具有明显不成比例的表现的群体的特征。在本文中，我们提出了一种新的分类器，即orpsw（具有重量的最佳风险和预防性集），以基于最佳风险和预防模式对基因表达数据进行分类。该方法已经在四个基准基因表达数据集上进行了测试，以比较三个最先进的分类器：C4.5，幼稚贝叶斯和SVM。实验表明，ORPSW分类器通常比C4.5和朴素的贝叶斯分类器更准确，并且与SVM分类器相当。观察到准确性对类的前提分配敏感，我们还使用了假阳性率（FPR）和假负速率（FNR），以更好地表征分类器的性能。 orpsw分类器在这尺度下也非常好。它在不同类别中提供差异表达的基因，这有助于更好地了解分类过程。

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