Empirical Evaluation of Cost Overrun Prediction with Imbalance Data
Empirical Evaluation of Cost Overrun Prediction with Imbalance Data
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DOI：
10.1109/icis.2011.71
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发表时间：
2011-05
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影响因子：
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通讯作者：
Masateru Tsunoda;Akito Monden;Jun-ichiro Shibata;Ken-ichi Matsumoto
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文献类型：
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作者：
Masateru Tsunoda;Akito Monden;Jun-ichiro Shibata;Ken-ichi Matsumoto
为了防止成本超支软件项目，项目经理有必要确定在早期阶段具有高成本超支风险的项目。到目前为止，诸如线性判别分析和逻辑回归之类的判别方法已被用来预测成本超支项目。但是，当用于预测的数据集不平衡时，判别方法的准确性通常会变得较低，即成本超支项目数量与非成本超支项目之间存在很大差异。在本文中，我们通过更改数据集中的成本超支项目的百分比来比较线性判别分析，逻辑回归，分类树，Mahalanobis-Taguchi方法和协作过滤的准确性。结果表明，在五种方法中，协作过滤的精度最高。当数据集中的成本超支项目的数量和非成本超支的数量是平衡的时，线性判别分析是第二高的精度，而当它不平衡时，马哈拉诺比蒂 - 塔古奇方法是五种方法中的第二高。
To prevent cost overrun of software projects, it is necessary for project managers to identify projects which have high risk of cost overrun in the early phase. So far, discriminant methods such as linear discriminant analysis and logistic regression have been used to predict cost overrun projects. However, accuracy of discriminant methods often becomes low when a dataset used for predict is imbalanced, i.e. there exists a large difference between the number of cost overrun projects and non cost overrun projects. In this paper, we compared accuracy of linear discriminant analysis, logistic regression, classification tree, Mahalanobis-Taguchi method, and collaborative filtering, by changing the percentage of cost overrun projects in the dataset. The result showed that collaborative filtering was highest accuracy among five methods. When the number of cost overrun projects and non cost overrun was balanced in the dataset, linear discriminant analysis was second highest accuracy, and when it was not balanced, Mahalanobis-Taguchi method was second highest among five methods.