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首页> 外文会议>IEEE Annual Computers, Software, and Applications Conference >SLA+: Narrowing the Difference between Data Sets in Heterogenous Cross-Project Defection Prediction
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SLA+: Narrowing the Difference between Data Sets in Heterogenous Cross-Project Defection Prediction

机译：SLA +：缩小异构跨项目缺陷预测中数据集之间的差异

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摘要

Different from existing cross-project defection prediction(CPDP) problems which assume that there is a close relation between the source data sets and the target data sets, in the heterogenous cross-project defection prediction(HCPDP) problem, the target data sets can be totally different from the source data sets. In order to narrow the difference between source data sets and target data sets, we implemented our own algorithm SLA + based on the selective learning algorithm . We select one of the multiple sources that have the highest similarity to the target data set as the source data set, and select one or more of the other source data sets that are similar to both the target data set and the source data set as an intermediate domain. We set up a bridge between the target domain and the source domain through the intermediate domain , breaking the large distribution gap for transferring knowledge between the source domain and the target domain. Besides, we achieve the purpose of dimensionality reduction by mining the potential relationship between features. We have done experiments on open source data sets, and the data sets used are all heterogeneous. The experiments prove that our method achieves comparable results compared with state-of-the-art HCPDP in most cases.
机译：与现有的跨项目缺陷预测（CPDP）问题（假设源数据集和目标数据集之间存在紧密关系）不同，在异构跨项目缺陷预测（HCPDP）问题中，目标数据集可以是与源数据集完全不同。为了缩小源数据集和目标数据集之间的差异，我们基于选择性学习算法实现了自己的算法SLA +。我们选择与目标数据集具有最高相似性的多个源之一作为源数据集，并选择与目标数据集和源数据集都相似的一个或多个其他源数据集作为源数据集。中间域。我们通过中间域在目标域和源域之间架起了一座桥梁，打破了在源域和目标域之间传递知识的巨大分配差距。此外，我们通过挖掘特征之间的潜在关系来达到降维的目的。我们已经在开源数据集上进行了实验，并且所使用的数据集都是异构的。实验证明，与大多数情况下的最新HCPDP相比，我们的方法可获得可比的结果。

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