Active learning with support vector machine applied to gene expression data for cancer classification
Active learning with support vector machine applied to gene expression data for cancer classification
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DOI：
10.1021/ci049810a
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发表时间：
2004-11-01
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影响因子：
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通讯作者：
Liu, Y
中科院分区：
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作者：
Liu, Y
人们对机器学习技术在生物信息学中的应用越来越感兴趣。监督机器学习方法已广泛应用于生物信息学，并在该研究领域取得了很大的成功。通过这种学习方法，研究人员首先开发一个大型训练集，这是一个耗时且昂贵的过程。此外，训练集中正例和负例的比例可能无法代表现实世界的数据分布，这会导致概念漂移。主动学习可以避免这些问题。与大多数用于推导模型的训练集保持静态的传统学习方法不同，分类器可以主动选择训练数据，并且训练集的大小会增加。我们引入了一种利用支持向量机进行主动学习的算法，并将该算法应用于结肠癌、肺癌和前列腺癌样本的基因表达谱。我们比较了主动学习和被动学习的分类性能。结果表明，采用主动学习方法可以实现高精度并显着减少对标记训练实例的需求。对于肺癌分类，要实现总阳性率的 96%，主动学习只需要 31 个标记示例，而被动学习则需要 174 个标记示例。这意味着超过 82% 的减少是通过主动学习实现的。在主动学习中，接受者操作特征 (ROC) 曲线下的面积超过 0.81，而在被动学习中，ROC 曲线下的面积低于 0.50。
There is growing interest in the application of machine learning techniques in bioinformatics. The supervised machine learning approach has been widely applied to bioinformatics and gained a lot of success in this research area. With this learning approach researchers first develop a large training set, which is a time-consuming and costly process. Moreover, the proportion of the positive examples and negative examples in the training set may not represent the real-world data distribution, which causes concept drift. Active learning avoids these problems. Unlike most conventional learning methods where the training set used to derive the model remains static, the classifier can actively choose the training data and the size of training set increases. We introduced an algorithm for performing active learning with support vector machine and applied the algorithm to gene expression profiles of colon cancer, lung cancer, and prostate cancer samples. We compared the classification performance of active learning with that of passive learning. The results showed that employing the active learning method can achieve high accuracy and significantly reduce the need for labeled training instances. For lung cancer classification, to achieve 96% of the total positives, only 31 labeled examples were needed in active learning whereas in passive learning 174 labeled examples were required. That meant over 82% reduction was realized by active learning. In active learning the areas under the receiver operating characteristic (ROC) curves were over 0.81, while in passive learning the areas under the ROC Curves were below 0.50.