The feature selection in analogy-based software effort estimation （ASEE） is formulized as a multi-objective optimization problem. One objective is designed to maximize the effort estimation accuracy and the other ob...The feature selection in analogy-based software effort estimation （ASEE） is formulized as a multi-objective optimization problem. One objective is designed to maximize the effort estimation accuracy and the other objective is designed to minimize the number of selected features. Based on these two potential conflict objectives, a novel wrapper- based feature selection method, multi-objective feature selection for analogy-based software effort estimation （MASE）, is proposed. In the empirical studies, 77 projects in Desharnais and 62 projects in Maxwell from the real world are selected as the evaluation objects and the proposed method MASE is compared with some baseline methods. Final results show that the proposed method can achieve better performance by selecting fewer features when considering MMRE （mean magnitude of relative error）, MdMRE （median magnitude of relative error）, PRED （ 0. 25 ）, and SA （ standardized accuracy） performance metrics.展开更多
A powerful platform of digital brain is proposed using crowd wisdom for brain research,based on the computational artificial intelligence model of synthesis reasoning and multi-source analogical generating.The design ...A powerful platform of digital brain is proposed using crowd wisdom for brain research,based on the computational artificial intelligence model of synthesis reasoning and multi-source analogical generating.The design of the platform aims to make it a comprehensive brain database,a brain phantom generator,a brain knowledge base,and an intelligent assistant for research on neurological and psychiatric diseases and brain development.Using big data,crowd wisdom,and high performance computers may significantly enhance the capability of the platform.Preliminary achievements along this track are reported.展开更多
基金The National Natural Science Foundation of China(No.61602267,61202006)the Open Project of State Key Laboratory for Novel Software Technology at Nanjing University(No.KFKT2016B18)
文摘The feature selection in analogy-based software effort estimation （ASEE） is formulized as a multi-objective optimization problem. One objective is designed to maximize the effort estimation accuracy and the other objective is designed to minimize the number of selected features. Based on these two potential conflict objectives, a novel wrapper- based feature selection method, multi-objective feature selection for analogy-based software effort estimation （MASE）, is proposed. In the empirical studies, 77 projects in Desharnais and 62 projects in Maxwell from the real world are selected as the evaluation objects and the proposed method MASE is compared with some baseline methods. Final results show that the proposed method can achieve better performance by selecting fewer features when considering MMRE （mean magnitude of relative error）, MdMRE （median magnitude of relative error）, PRED （ 0. 25 ）, and SA （ standardized accuracy） performance metrics.
基金supported by the National Key R&D Program of China(No.2017YFC1308502)the National Natural Science Foundation of China(No.81471734)
文摘A powerful platform of digital brain is proposed using crowd wisdom for brain research,based on the computational artificial intelligence model of synthesis reasoning and multi-source analogical generating.The design of the platform aims to make it a comprehensive brain database,a brain phantom generator,a brain knowledge base,and an intelligent assistant for research on neurological and psychiatric diseases and brain development.Using big data,crowd wisdom,and high performance computers may significantly enhance the capability of the platform.Preliminary achievements along this track are reported.
