全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

二次回归学习及其在软件开发工作量预测上的应用*

DOI: 10.16451/j.cnki.issn1003-6059.201501008, PP. 59-64

Keywords: 回归分析,机器学习,二次回归学习,软件挖掘,工作量预测

Full-Text   Cite this paper   Add to My Lib

Abstract:

回归学习是用于对具有实值标记样本进行学习建模的监督学习技术.为获得良好的预测性能,通常需要大量的训练样本,然而,在实际应用中可收集到的训练样本数量极少.针对该问题,提出一种基于二次学习框架的新型二次回归学习方法——基于神经网络集成的回归树算法(NERT).该方法借助虚拟样本生成技术,通过串行执行的两个学习阶段对其进行有效利用,有效缓解训练样本不足的困难,从而提升学习性能.同时,通过为两个阶段分别选择泛化能力强和理解性好的学习方法,可得到预测性能好且可理解性高的模型.实验结果表明在训练样本极少的软件开发工作量预测问题上,NERT方法能够从小样本数据得到比现有方法更好的预测性能,同时其模型内在可理解性能够揭示工作量预测的关键因素.

References

[1]  Mjolsness E, DeCoste D. Machine Learning for Science: State of the Art and Future Prospects. Science, 2001, 293(5537): 2051-2055
[2]  Fox J. Applied Regression Analysis, Linear Models and Related Methods. New York, USA: Sage Publications, Inc, 1997
[3]  Dejaeger K, Verbeke W, Martens D, et al. Data Mining Techniques for Software Effort Estimation: A Comparative Study. IEEE Trans on Software Engineering, 2012, 38(2): 375-397
[4]  Park H, Baek S. An Empirical Validation of a Neural Network Model for Software Effort Estimation. Expert Systems with Applications, 2008, 35(3): 929-937
[5]  Finnie G R, Wittig G E, Desharnais J M. A Comparison of Software Effort Estimation Techniques: Using Function Points with Neural Network, Case-Based Reasoning and Regression Models. Journal of Systems and Software, 1997, 39(3): 281-289
[6]  Breiman L, Friedman J, Stone C J, et al. Classification and Regre-ssion Trees. Boca Raton, USA: Chapman and Hall/CRC, 1984
[7]  Schapire R E. The Strength of Weak Learnability. Machine Learning, 1990, 5(2): 197-227
[8]  Zhou Z H, Jiang Y. Medical Diagnosis with C4.5 Rule Preceded by Artificial Neural Network Ensemble. IEEE Trans on Information Technology in Biomedicine, 2003, 7(1): 37-42
[9]  Zhou Z H, Jiang Y. NeC4.5: Neural Ensemble Based C4.5. IEEE Trans on Knowledge and Data Engineering, 2004, 16(6): 770-773
[10]  Jiang Y, Li M, Zhou Z H. Mining Extremely Small Data Sets with Application to Software Reuse. Software: Practice and Experience, 2009, 39(4): 423-440
[11]  Kocaguneli E, Menzies T, Keung J W. On the Value of Ensemble Effort Estimation. IEEE Trans on Software Engineering, 2012, 38(6): 1403-1416
[12]  Zhou Z H, Li M. Semi-Supervised Regression with Co-training Style Algorithms. IEEE Trans on Knowledge and Data Engineering, 2007, 19(11): 1479-1493
[13]  Menzies T, Caglayan B, Kocaguneli E, et al. The PROMISE Repository of Empirical Software Engineering Data. [DB/OL]. [2012-6-15]. http://promisedata.googlecode.com
[14]  Albrecht A J, Gaffney J E. Software Function, Source Lines of Code, and Development Effort Prediction: A Software Science Va-lidation. IEEE Trans on Software Engineering, 1983, SE-9(6): 639-648
[15]  Boehm B W. Software Engineering Economics. Enghewood, USA: Prentice Hall, 1981
[16]  Hyndman R J, Koehler A B. Another Look at Measures of Forecast Accuracy. International Journal of Forecasting, 2006, 22(4): 679-688

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133