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Hierarchical error-correcting output codes based on SVDD

机译:基于SVDD的分层纠错输出代码

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Error-correcting output codes (ECOC) can effectively reduce the multiclass to the binary and is attracting close attention, in which the construction of coding matrix based on data is the key to use ECOC to solve multiclass problems. An approach to the hierarchical error-correcting output codes based on support vector data description is presented in this paper. The main idea of the work is to construct the data-driven coding matrix with the help of support vector data description and binary tree. The support vector data description is used to measure the class separability quantitatively to obtain the inter-class separability matrix. And, a binary tree is built based on the matrixes from bottom to top. Then, each node of each layer is encoded to get the final hierarchical error-correcting output code. The independence of base classifiers trained by different encoding methods is compared in experiments. The results show that the proposed technique can promote the diversity of the base classifiers and enhance the classification accuracy.
机译:纠错输出码(ECOC)可以有效地将多类减少为二进制,并引起了广泛关注,其中基于数据的编码矩阵的构造是使用ECOC解决多类问题的关键。提出了一种基于支持向量数据描述的分层纠错输出码的方法。这项工作的主要思想是借助支持向量数据描述和二叉树来构造数据驱动的编码矩阵。支持向量数据描述用于量化类可分离性,以获得类间可分离性矩阵。并且,基于从下到上的矩阵构建二叉树。然后,对每一层的每个节点进行编码以获得最终的分层纠错输出代码。在实验中比较了通过不同编码方法训练的基本分类器的独立性。结果表明,该技术可以促进基本分类器的多样性,提高分类精度。

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