کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4956387 | 1444515 | 2017 | 64 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Cross-validation based K nearest neighbor imputation for software quality datasets: An empirical study
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کلمات کلیدی
GRGMCARRMSEGRCMDTMARKNNMEI - MAYMutual information - اطلاعات متقابلCross-validation - اعتبار سنجی متقابلGRA - بازیGrey relational analysis - تجزیه و تحلیل رابطه ای خاکستریMissing data - داده های گم شدهGrey relational grade - درجه ارتباطی خاکستریClassification accuracy - دقت طبقه بندیSEE - دیدنRoot mean square error - ریشه میانگین خطای مربعresearch question - سوال پژوهشیBMI - شاخص توده بدنیSVM - ماشین بردار پشتیبانیSupport vector machine - ماشین بردار پشتیبانیImputation - محاسبهLoc - محلPROMISE - وعدهk nearest neighbor - ک نزدیکترین همسایهFault-proneness - گسستگیmissing at random - گم شده در تصادفیMissing completely at random - گمشده به طور تصادفی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
شبکه های کامپیوتری و ارتباطات
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چکیده انگلیسی
Being able to predict software quality is essential, but also it pose significant challenges in software engineering. Historical software project datasets are often being utilized together with various machine learning algorithms for fault-proneness classification. Unfortunately, the missing values in datasets have negative impacts on the estimation accuracy and therefore, could lead to inconsistent results. As a method handling missing data, K nearest neighbor (KNN) imputation gradually gains acceptance in empirical studies by its exemplary performance and simplicity. To date, researchers still call for optimized parameter setting for KNN imputation to further improve its performance. In the work, we develop a novel incomplete-instance based KNN imputation technique, which utilizes a cross-validation scheme to optimize the parameters for each missing value. An experimental assessment is conducted on eight quality datasets under various missingness scenarios. The study also compared the proposed imputation approach with mean imputation and other three KNN imputation approaches. The results show that our proposed approach is superior to others in general. The relatively optimal fixed parameter settings for KNN imputation for software quality data is also determined. It is observed that the classification accuracy is improved or at least maintained by using our approach for missing data imputation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Systems and Software - Volume 132, October 2017, Pages 226-252
Journal: Journal of Systems and Software - Volume 132, October 2017, Pages 226-252
نویسندگان
Huang Jianglin, Keung Jacky Wai, Federica Sarro, Li Yan-Fu, Yu Y.T., Chan W.K., Sun Hongyi,