数据复杂度对分类器性能的影响
Jonas Eberlein1, Daniel Rodriguez1,2, Rachel Harrison1
1School of Technology, Oxford Brookes University, Headington Campus, Oxford, OX3 0BP UK.
概括
软件缺陷预测 (SDP) 模型面临性能上限. 分析数据的复杂性表明,分类器的性能因数据集而异,一些模型在特定情况下表现出色.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
背景情况:
- 软件缺陷预测 (SDP) 是一个受欢迎的研究领域,通常被视为分类问题.
- 尽管在分类,预处理和调整方面取得了进展,但SDP模型经常达到性能上限.
- 这表明了超越标准模型优化技术的局限性.
研究的目的:
- 从数据复杂性的角度分析SDP中的分类器性能.
- 研究数据复杂度指标与各种机器学习分类器的性能之间的相关性.
- 在各种数据集中确定不同分类器的特定优点和弱点.
主要方法:
- 使用统一错误数据集计算数据复杂度指标,该数据集是众所周知的SDP数据集的汇编.
- 评估了机器学习分类器的性能,包括C5.0,天真贝叶斯,人工神经网络,随机森林和支持向量机器.
- 与分类器性能相关联的数据复杂度指标,以了解它们之间的关系.
主要成果:
- 对不同的分类器来说,识别出了不同的能力和无能力领域.
- 在分类器性能和它们与性能指标的关系中发现了相似之处和差异.
- 证明数据复杂性是影响SDP模型性能的关键因素.
- 观察到某些分类器在特定的数据复杂性条件下表现最佳.
结论:
- 软件缺陷预测中的分类器性能高度依赖于数据的复杂性.
- 没有单一的分类器是普遍优越的;最佳选择取决于特定的数据集特征.
- 数据复杂度指标为了解和潜在地改善SDP模型性能提供了有价值的见解.
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