<div style="text-align:justify;"> <span style="font-family:Verdana;">Software Cost Estimation (SCE) is an essential requirement in producing software these days. Genuine accurate estima...<div style="text-align:justify;"> <span style="font-family:Verdana;">Software Cost Estimation (SCE) is an essential requirement in producing software these days. Genuine accurate estimation requires cost-and-efforts factors in delivering software by utilizing algorithmic or Ensemble Learning Methods (ELMs). Effort is estimated in terms of individual months and length. Overestimation as well as underestimation of efforts can adversely affect software development. Hence, it is the responsibility of software development managers to estimate the cost using the best possible techniques. The predominant cost for any product is the expense of figuring effort. Subsequently, effort estimation is exceptionally pivotal and there is a constant need to improve its accuracy. Fortunately, several efforts estimation models are available;however, it is difficult to determine which model is more accurate on what dataset. Hence, we use ensemble learning bagging with base learner Linear regression, SMOReg, MLP, random forest, REPTree, and M5Rule. We also implemented the feature selection algorithm to examine the effect of feature selection algorithm BestFit and Genetic Algorithm. The dataset is based on 499 projects known as China. The results show that the Mean Magnitude Relative error of Bagging M5 rule with Genetic Algorithm as Feature Selection is 10%, which makes it better than other algorithms.</span> </div>展开更多
多准则决策分析已广泛用于卫生决策领域。通过分类方法测量属性(measuring attractiveness by a categorical based evaluation technique,MACBETH)是一种多准则决策分析方法,该方法与其他价值测量方法的关键区别在于MACBETH使用了对吸...多准则决策分析已广泛用于卫生决策领域。通过分类方法测量属性(measuring attractiveness by a categorical based evaluation technique,MACBETH)是一种多准则决策分析方法,该方法与其他价值测量方法的关键区别在于MACBETH使用了对吸引力差异的定性判断,以为选项生成价值分数。M-MACBETH软件可实现MACBETH分析过程,包括价值树的结构化、建立价值函数、对备选方案的各准则评分、对准则赋权、一致性检验及敏感性分析等。本文主要介绍了MACBETH的基本原理及如何使用M-MACBETH软件实现多准则决策分析。展开更多
文摘<div style="text-align:justify;"> <span style="font-family:Verdana;">Software Cost Estimation (SCE) is an essential requirement in producing software these days. Genuine accurate estimation requires cost-and-efforts factors in delivering software by utilizing algorithmic or Ensemble Learning Methods (ELMs). Effort is estimated in terms of individual months and length. Overestimation as well as underestimation of efforts can adversely affect software development. Hence, it is the responsibility of software development managers to estimate the cost using the best possible techniques. The predominant cost for any product is the expense of figuring effort. Subsequently, effort estimation is exceptionally pivotal and there is a constant need to improve its accuracy. Fortunately, several efforts estimation models are available;however, it is difficult to determine which model is more accurate on what dataset. Hence, we use ensemble learning bagging with base learner Linear regression, SMOReg, MLP, random forest, REPTree, and M5Rule. We also implemented the feature selection algorithm to examine the effect of feature selection algorithm BestFit and Genetic Algorithm. The dataset is based on 499 projects known as China. The results show that the Mean Magnitude Relative error of Bagging M5 rule with Genetic Algorithm as Feature Selection is 10%, which makes it better than other algorithms.</span> </div>
文摘多准则决策分析已广泛用于卫生决策领域。通过分类方法测量属性(measuring attractiveness by a categorical based evaluation technique,MACBETH)是一种多准则决策分析方法,该方法与其他价值测量方法的关键区别在于MACBETH使用了对吸引力差异的定性判断,以为选项生成价值分数。M-MACBETH软件可实现MACBETH分析过程,包括价值树的结构化、建立价值函数、对备选方案的各准则评分、对准则赋权、一致性检验及敏感性分析等。本文主要介绍了MACBETH的基本原理及如何使用M-MACBETH软件实现多准则决策分析。
