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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Empirical assessment of feature selection techniques in defect prediction models using web applications
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Empirical assessment of feature selection techniques in defect prediction models using web applications

机译：Web应用程序缺陷预测模型中特征选择技术的经验评估

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摘要

In order to minimize the over-fitting and related factors that are caused by the high dimensionality of the input data in software defect prediction, the attributes are often optimized using various feature selection techniques. However, the comparative performance of these selection techniques in combination with machine learning algorithms remains largely unexplored using web applications. In this work, we investigate the best possible combination of feature selection technique with machine learning algorithms, with the sample space chosen from open source Apache Click and Rave data sets. Our results are based on 945 defect prediction models derived from parametric, non-parametric and ensemble-based machine learning algorithms, for which the metrics are derived from the various filter and threshold-based ranking techniques. Friedman and Nemenyi post-hoc statistical tests are adopted to identify the performance difference of these models. We find that filter-based feature selection in combination with ensemble-based machine learning algorithms not only poise as the best strategy but also yields a maximum feature set redundancy by 94%, with little or no comprise on the performance index.
机译：为了最小化由软件缺陷预测中输入数据的高维度引起的过度拟合和相关因素，通常使用各种特征选择技术进行优化属性。然而，这些选择技术与机器学习算法组合的比较性能在很大程度上使用Web应用程序仍未开发。在这项工作中，我们调查了具有机器学习算法的特征选择技术的最佳组合，采用从开源Apache选择的示例空间点击和rave数据集。我们的结果基于来自参数，非参数和集合的机器学习算法的945个缺陷预测模型，其中度量来自各种滤波器和基于阈值的排名技术。采用弗里德曼和涅斯·涅伊统计检验来确定这些模型的绩效差异。我们发现基于过滤器的特征选择与基于集合的机器学习算法的组合，不仅是作为最佳策略的Poise，而且还产生了94％的最大功能集冗余，很少或不包括性能指数。

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