Hybrid ensemble learning approaches for cancer classification from gene expression data

Cao Truong Tran
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Abstract

The expression levels of genes is well-recognised to hold the keys to address many fundamental biological problems. A major application of such datasets is cancer diagnosis which is essentially a classification task. Ensemble learning, which is a powerful machine learning approach, has been widely used to improve the performance of many real-world classification problems. Ensemble learning has been also applied for cancer classification from gene expression data. This paper proposed two hybrid ensemble machine learning approaches for classifying cancer gene expression data. The first approach is the integration of random subspace ensemble with bagging, and the second one is the integration of random subspace ensemble with boosting. Experimental results show that the proposed methods can improve classification accuracy for cancer classification from gene expression data.
基于基因表达数据的癌症分类的混合集成学习方法
众所周知,基因的表达水平是解决许多基本生物学问题的关键。这些数据集的一个主要应用是癌症诊断,这本质上是一个分类任务。集成学习是一种强大的机器学习方法,已被广泛用于提高许多现实世界分类问题的性能。集成学习也被应用于从基因表达数据中进行癌症分类。本文提出了两种用于癌症基因表达数据分类的混合集成机器学习方法。第一种方法是随机子空间系综与bagging的积分,第二种方法是随机子空间系综与boosting的积分。实验结果表明,该方法能够提高基于基因表达数据的癌症分类准确率。
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