Combining multiple decisions: applications to bioinformatics
Combining multiple decisions: applications to bioinformatics
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DOI：
10.1088/1742-6596/95/1/012018
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发表时间：
2008
期刊：
影响因子：
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通讯作者：
Naoto Yukinawa;Takashi Takenouchi;Shigeyuki Oba;S. Ishii
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文献类型：
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作者：
Naoto Yukinawa;Takashi Takenouchi;Shigeyuki Oba;S. Ishii
多类分类是生物信息学的基本任务之一，通常出现在基因表达谱的癌症诊断研究中。本文综述了两种基于纠错输出编码（ECOC）统一框架的组合多分类器的多类分类方法。第一种方法是构建一个多类分类器，其中每个要聚合的二元分类器都有一个权重值，以便根据观察到的数据进行最佳调优。在第二种方法中，通过类比信息传输理论的上下文，将每个二元分类器的误分类用概率模型表示为位反转误差。使用包括癌症分类问题在内的各种真实世界数据集进行的实验研究表明，这两种新方法都优于或可与其他多类分类方法相媲美。
Multi-class classification is one of the fundamental tasks in bioinformatics and typically arises in cancer diagnosis studies by gene expression profiling. This article reviews two recent approaches to multi-class classification by combining multiple binary classifiers, which are formulated based on a unified framework of error-correcting output coding (ECOC). The first approach is to construct a multi-class classifier in which each binary classifier to be aggregated has a weight value to be optimally tuned based on the observed data. In the second approach, misclassification of each binary classifier is formulated as a bit inversion error with a probabilistic model by making an analogy to the context of information transmission theory. Experimental studies using various real-world datasets including cancer classification problems reveal that both of the new methods are superior or comparable to other multi-class classification methods.