文章基本信息

标题：A Novel Integrated Random Forest and Gradient Boosting Machine Technique for Software Reuse Analytic
本地全文：下载
作者：Pawandeep Kaur ; Pankaj Deep Kaur
期刊名称：International Journal of Advanced Research in Computer Engineering & Technology (IJARCET)
印刷版ISSN：2278-1323
出版年度：2017
卷号：6
期号：6
页码：957-965
出版社：Shri Pannalal Research Institute of Technolgy
摘要：Cleaner production (CP) nowadays is considered as the important mean for production companies to get the sustainable production. However, there are various parameters of the CP like recycling, production cost, reuse, energy consumption and minimization of waste material, which help for the successful implementation of CP process for manufacturing and maintenance processes (MMP). Big data based analytics for product lifecycle (BDA-PL) architecture is one of the various architectures which helps to get the better CP and product lifecycle management(PLM) decisions based on the large amount of heterogeneous big data. Software reuse is process of producing new products or software from the present software by making some changes. This paper presents an integrated Random forest and Gradient Boosting machine (GBM) technique for software reuse analytics which improves the matrices like accuracy, error rate, Mean Absolute Error (MAE) and Relative Absolute Error (RAE).The result shows that there is 20% increase in performance of proposed algorithm with respect to existing algorithm.
关键词：Cleaner production; Big data analytics; BDA-PL architecture; PLM; Software reuse; Random forest; Gradient Boosting Machine; Accuracy; Error rate