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Adaptive PSO Based Association Rule Mining Technique for Software Defect Classification Using ANN

机译:基于ANN的自适应PSO关联规则挖掘技术在软件缺陷分类中的应用。

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The proposed system categorizes various defects by using association rule mining dependent problem classification approach, which is applied to collect the actual defects using recognition. Association rule mining algorithm at times results in useless policies. To avoid this kind of concerns, the principles prior to classification determined by assistance as well as confidence value has to be optimized. In this exploration, Adaptive Particle Swarm (APSO) optimization algorithm is used. This can discover the best assistance and confidence value to have the best policies. And finally Artificial Neural Network (ANN) can be used to classify the actual defects determined.
机译:所提出的系统通过使用关联规则挖掘相关的问题分类方法对各种缺陷进行分类,该方法用于通过识别来收集实际缺陷。关联规则挖掘算法有时会导致无用的策略。为了避免此类问题,必须优化由协助确定的分类之前的原则以及置信度值。在此探索中,使用了自适应粒子群算法(APSO)。这样可以发现最好的帮助和信心值,以拥有最好的策略。最后,人工神经网络(ANN)可用于对确定的实际缺陷进行分类。

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