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Selecting Potentially Relevant Records Using Re-identification Methods

机译:使用重新识别方法选择潜在相关记录

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This work proposes re-identification algorithms to select records that are interesting from the point of view of giving new information. Instead of focusing on re-identified elements, we focus on non re-identified records (non linked records) as they are the ones that potentially supply new and relevant information. Moreover, these relevant characteristics can correspond to chances for improving the knowledge of a system. To evaluate our approach, we have applied it to a example using publicly available data from the UCI repository. We have used the data of the ionosphere data base to build a re-identification problem for 35 non-common variables. We show that the use of a simple heuristic rule base can effectively select potentially interesting records.
机译:这项工作提出了重新识别算法,以从提供新信息的角度选择有趣的记录。我们不关注重新标识的元素,而是关注未重新标识的记录(非链接记录),因为它们是潜在提供新信息和相关信息的记录。而且,这些相关特征可以对应于改善系统知识的机会。为了评估我们的方法,我们已使用UCI存储库中公开可用的数据将其应用于示例。我们已经使用电离层数据库的数据为35个非公共变量建立了重新识别问题。我们表明,使用简单的启发式规则库可以有效地选择潜在的有趣记录。

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