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Adaptive ridge regression system for software cost estimating on multi-collinear datasets 总被引:1,自引:0,他引:1
Yan-Fu Li Author Vitae Min Xie Author Vitae Author Vitae 《Journal of Systems and Software》2010,83(11):2332-2343
Cost estimation is one of the most critical activities in software life cycle. In past decades, a number of techniques have been proposed for cost estimation. Linear regression is yet the most frequently applied method in the literature. However, a number of studies point out that linear regression is prone to low prediction accuracy. The low prediction accuracy is due to a number of reasons such as non-linearity and non-normality. One less addressed reason is the multi-collinearities which may lead to unstable regression coefficients. On the other hand, it has been reported that multi-collinearity spreads widely across the software engineering datasets. To tackle this problem and improve regression's accuracy, we propose a holistic problem-solving approach (named adaptive ridge regression system) integrating data transformation, multi-collinearity diagnosis, ridge regression technique and multi-objective optimization. The proposed system is tested on two real world datasets with the comparisons with OLS regression, stepwise regression and other machine learning methods. The results indicate that adaptive ridge regression system can significantly improve the performance of regressions on multi-collinear datasets and produce more explainable results than machine learning methods. 相似文献
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Statistical optimization and assessment of a thermal error model for CNC machine tools 总被引:1,自引:0,他引:1
The objective of a thermal error compensation system for CNC machine tools is improved machining accuracy through real time error compensation. The compensation capability depends on the accuracy of the thermal error model. A thermal error model can be obtained using an appropriate combination of temperature variables. In this study, the thermal error modeling is based on a correlation grouping and a successive linear regression analysis. During the successive regression analysis, the residual mean square is minimized using a judgement function, which, although simple, is effective in the selection of variables in the error model. When evaluating the proposed thermal error model, the multi-collinearity problem and computational time are both improved through the correlation grouping, and the linear model is more robust against measurement noises than the engineering judgement model, which includes variables with higher order terms. The modeling method used in this study can be effectively and practically applied to real-time error compensation because it includes the advantages of simple application, reduced computational time, sufficient model accuracy, and model robustnesss. 相似文献
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