Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation

Gonzalez P.; Alvarez E.; Barranquero J.; Diez J.

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Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation

机译：具有示例相关成本的多类支持向量机应用于浮游生物的生物量估计

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In many applications, the mistakes made by an automatic classifier are not equal, they have different costs. These problems may be solved using a cost-sensitive learning approach. The main idea is not to minimize the number of errors, but the total cost produced by such mistakes. This brief presents a new multiclass cost-sensitive algorithm, in which each example has attached its corresponding misclassification cost. Our proposal is theoretically well-founded and is designed to optimize cost-sensitive loss functions. This research was motivated by a real-world problem, the biomass estimation of several plankton taxonomic groups. In this particular application, our method improves the performance of traditional multiclass classification approaches that optimize the accuracy.

机译：在许多应用中，自动分类器所犯的错误是不相等的，它们具有不同的成本。这些问题可以使用对成本敏感的学习方法来解决。主要思想不是减少错误的数量，而是减少此类错误产生的总成本。本简介介绍了一种新的多类别成本敏感算法，其中每个示例都附加了其相应的误分类成本。我们的建议从理论上讲是有根据的，旨在优化对成本敏感的损失函数。这项研究是受一个现实世界的问题推动的，即几个浮游生物分类群的生物量估计。在此特定应用程序中，我们的方法提高了优化精度的传统多类分类方法的性能。

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来源
《Neural Networks and Learning Systems, IEEE Transactions on》 |2013年第11期|1901-1905|共5页
作者
Gonzalez P.; Alvarez E.; Barranquero J.; Diez J.;
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作者单位

Artificial Intelligence Center, University of Oviedo, Gijón, Spain|c|;

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正文语种 eng
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关键词
Cost-sensitive learning; SVM; example-dependent costs; kernel methods; plankton recognition;

机译：成本敏感型学习;支持向量机;与示例相关的成本;内核方法;浮游生物识别;

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Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation

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