Classifying Molecules Using a Sparse Probabilistic Kernel Binary Classifier
Classifying Molecules Using a Sparse Probabilistic Kernel Binary Classifier
复制标题
DOI：
10.1021/ci200128w
复制
发表时间：
2011-07
影响因子：
5.6
通讯作者：
Robert Lowe;H. Mussa;John B. O. Mitchell;R. Glen
中科院分区：
文献类型：
--
作者：
Robert Lowe;H. Mussa;John B. O. Mitchell;R. Glen
化学信息学中监督分类的核心思想是设计一种分类算法，将一个新的分子准确地分配到一组预定义的类别中的一个。TIPING设计了一种分类方案--相关性向量机(RVM)，它在稀疏性方面相当于支持向量机(SVM)。然而，与支持向量机分类器不同，RVM分类器本质上是概率的，这在决策和风险承担领域是至关重要的。在这项工作中，我们研究了RVM二进制分类器在将MDDR数据集的子集-标准分子基准数据集-分类为活性和非活性化合物方面的性能。此外，我们还给出了比较支持向量机和RVM二进制分类器性能的结果。
The central idea of supervised classification in chemoinformatics is to design a classifying algorithm that accurately assigns a new molecule to one of a set of predefined classes. Tipping has devised a classifying scheme, the Relevance Vector Machine (RVM), which is in terms of sparsity equivalent to the Support Vector Machine (SVM). However, unlike SVM classifiers, the RVM classifiers are probabilistic in nature, which is crucial in the field of decision making and risk taking. In this work, we investigate the performance of RVM binary classifiers on classifying a subset of the MDDR data set, a standard molecular benchmark data set, into active and inactive compounds. Additionally, we present results that compare the performance of SVM and RVM binary classifiers.