کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
388051 660915 2009 5 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Improved estimation of software project effort using multiple additive regression trees
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Improved estimation of software project effort using multiple additive regression trees
چکیده انگلیسی

Accurate estimation of software project effort is crucial for successful management and control of a software project. Recently, multiple additive regression trees (MART) has been proposed as a novel advance in data mining that extends and improves the classification and regression trees (CART) model using stochastic gradient boosting. This paper empirically evaluates the potential of MART as a novel software effort estimation model when compared with recently published models, in terms of accuracy. The comparison is based on a well-known and respected NASA software project dataset. The results indicate that improved estimation accuracy of software project effort has been achieved using MART when compared with linear regression, radial basis function neural networks, and support vector regression models.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 36, Issue 7, September 2009, Pages 10774–10778
نویسندگان
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