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Genetic algorithms in relevance feedback: a second test and new contributions

机译:相关反馈中的遗传算法:第二次测试和新的贡献

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摘要

The present work is the continuation of an earlier study which reviewed the literature on relevance feedback genetic techniques that follow the vector space model (the model that is most commonly used in this type of application), and implemented them so that they could be compared with each other as well as with one of the best traditional methods of relevance feedback―the Ide dec-hi method. We here carry out the comparisons on more test collections (Cranfield, CISI, Medline, and NPL), using the residual collection method for their evaluation as is recommended in this type of technique. We also add some fitness functions of our own design.
机译:本工作是先前研究的延续,该研究回顾了遵循向量空间模型(这种类型的应用程序中最常用的模型)的相关反馈遗传技术的文献,并对其进行了实现,以便可以将它们与以及最佳的传统相关性反馈方法之一-Ide dec-hi方法。我们在此对更多测试集合(Cranfield,CISI,Medline和NPL)进行比较,使用这种技术推荐的残差收集方法进行评估。我们还添加了一些自己设计的健身功能。

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