Prediction Algorithm for Software Defect Series Based on Nonlinear Weighted Ensemble Learning

doi:10.7652/xjtuxb201707022

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Prediction Algorithm for Software Defect Series Based on Nonlinear Weighted Ensemble Learning

更新时间：2025-07-09

- Prediction Algorithm for Software Defect Series Based on Nonlinear Weighted Ensemble Learning
- Vol. 51, Issue 7, Pages: 156-161(2017)
- 作者机构：
  
  西安交通大学电子与信息工程学院学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201707022
  CLC： O121.8
- Online First：10 July 2017，
  
  Published：2017
- 稿件说明：
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Prediction Algorithm for Software Defect Series Based on Nonlinear Weighted Ensemble Learning[J]. 2017, 51(7): 156-161.
DOI：

Prediction Algorithm for Software Defect Series Based on Nonlinear Weighted Ensemble Learning[J]. 2017, 51(7): 156-161. DOI： 10.7652/xjtuxb201707022.

摘要

针对当前软件缺陷序列预测算法准确度不高的问题

提出了基于非线性加权的集成学习软件缺陷序列预测算法(NLWEPrediction)。该算法在常见线性集成预测算法的基础上增加了非线性回归项

回归项代表了集成预测算法中基预测算法之间的相互关系

修正了线性集成预测的偏差

并通过梯度下降法求解了模型中的参数。实验表明:NLWEPrediction在14个软件缺陷数据集上的均方误差均小于250

并且平均绝对误差均小于13。通过与基预测算法、集成预测Bagging、Stacking算法和只考虑两个分类器关系的非线性加权集成学习算法进行对比

可以看出

NLWEPrediction预测算法的均方误差和平均绝对误差显著减小

预测精度显著提高

说明在线性集成预测算法基础上增加非线性回归项

能够有效提高集成学习算法的分类效果。

Abstract

Aiming at the problem of the basic prediction algorithm with relative low prediction accuracy

a novel and improved prediction algorithm NLWEPrediction is proposed based on non-linear weighted and ensemble learning. It combines the advantages of linear ensemble learning and the relationship between the base predict algorithms

which corrects the prediction deviation and uses gradient descent method to calculate the model parameters. The experiments proved that the NLWEPrediction's mean squared error in datasets is lower than 250

and the mean absolute difference is lower than 13. The algorithm was compared with its four base prediction algorithms

other two ensemble prediction algorithms Bagging and Stacking and original NLWEPrediction for efficiency analysis. Experimental results showed that NLWEPrediction has obviously low mean square error and average absolute error. The prediction accuracy is improved. So

adding the nonlinear regression terms can improve the capability of ensemble classifier.

关键词

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references

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