Locally weighted regression with different kernel smoothers for software effort estimation - Alqasrawi, Yousef; Azzeh, Mohammad; Elsheikh, Yousef (Science of computer programming, 2022)
•Locally Weighted Regression method provides an efficient solution to learn from local effort data.•A comprehensive experiment with 1080 Locally Weighted Regression variants has been constructed.•It has been fount that Uniform kernel functions cannot outperform non-uniform kernel functions.•Kernel type, polynomial degrees and bandwidth parameters do not produce a definite effect on estimation accuracy.•Locally Weighted Regression with Triweight or Triangle kernel outperform other complex kernels. Estimating software effort has been a largely unsolved problem for decades. One of the main reasons that hinders building accurate estimation models is the often heterogeneous nature of software data with a complex structure. Typically, building effort estimation models from local data tend to be more accurate than using the entire data. Previous studies have focused on the use of clustering techniques and decision trees to generate local and coherent data that can help in building local prediction models. However, these approaches may fall short in some aspect due to limitations in finding optimal clusters and processing noisy data. In this paper we used a more sophisticated locality approach that can mitigate these shortcomings that is Locally Weighted Regression (LWR). This method provides an efficient solution to learn from local data by building an estimation model that combines multiple local regression models in k-nearest-neighbor based model. The main factor affecting the accuracy of this method is the choice of the kernel function used to derive the weights for local regression models. This paper investigates the effects of choosing different kernels on the performance of Locally Weighted Regression of a software effort estimation problem. After comprehensive experiments with 7 datasets, 10 kernels, 3 polynomial degrees and 4 bandwidth values with a total of 840 Locally Weighted Regression variants, we found that: 1) Uniform kernel functions cannot outperform non-uniform kernel functions, and 2) kernel type, polynomial degrees and bandwidth parameters have no specific effect on the estimation accuracy. In other words, no change in bandwidth or degree values occurred with a significant difference in kernel rankings. In short, Locally Weighted Regression methods with Triweight or Triangle kernel can perform better than more complex kernels. Hence, we encourage non-uniform kernel methods as smoother function with wide bandwidth and small polynomial degree.