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首页> 外文会议>International conference on advances in computing, communications and informatics >Centroid based Binary Tree Structured SVM for multi classification
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Centroid based Binary Tree Structured SVM for multi classification

机译：基于质心的二叉树结构化SVM用于多分类

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Support Vector Machines (SVMs) were primarily designed for 2-class classification. But they have been extended for N-class classification also based on the requirement of multiclasses in the practical applications. Although N-class classification using SVM has considerable research attention, getting minimum number of classifiers at the time of training and testing is still a continuing research. We propose a new algorithm CBTS-SVM (Centroid based Binary Tree Structured SVM) which addresses this issue. In this we build a binary tree of SVM models based on the similarity of the class labels by finding their distance from the corresponding centroids at the root level. The experimental results demonstrates the comparable accuracy for CBTS with OVO with reasonable gamma and cost values. On the other hand when CBTS is compared with OVA, it gives the better accuracy with reduced training time and testing time. Furthermore CBTS is also scalable as it is able to handle the large data sets.
机译：支持向量机（SVM）主要设计用于2级分类。但是，他们还基于实际应用中的多标菌的要求，他们已经延长了N级分类。虽然使用SVM的N类分类具有相当大的研究，但在培训和测试时获得最小数量的分类器仍然是一个持续的研究。我们提出了一种新的算法CBTS-SVM（基于质心的二叉树结构化SVM），它解决了这个问题。在此，我们通过在根级别找到其距离相应质心的距离来构建基于类标签的相似性的SVM模型的二叉树。实验结果表明，具有合理的伽马和成本值的卵子的CBT的可比精度。另一方面，当CBT与OVA进行比较时，它可以通过减少训练时间和测试时间来提供更好的准确性。此外，CBT也可以扩展，因为它能够处理大数据集。

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