整合全球和地方促进

2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA) Pub Date : 2015-07-06 DOI:10.1109/IISA.2015.7388123

A. Lipitakis, G. Antzoulatos, S. Kotsiantis, M. Vrahatis

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引用次数: 0

摘要

一些数据分析问题需要研究相关异构数据库中属性之间的关系，其中不同的预测模型可能更适合不同的区域。提出了一种综合全局和局部增强的新方法。在标准基准数据集上与其他已知和广泛使用的组合方法进行了比较，结果表明该方法可以获得更准确的结果。

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Integrating global and local boosting

Several data analysis problems require investigations of relationships between attributes in related heterogeneous databases, where different prediction models can be more appropriate for different regions. A new technique of integrating global and local boosting is proposed. A comparison with other well known and widely used combining methods on standard benchmark datasets has shown that the proposed technique leads to more accurate results.

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2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)

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