论文标题
根据基于规则的加权专家系统选择合格的大学教练
Choose qualified instructor for university based on rule-based weighted expert system
论文作者
论文摘要
在整个大学附近，教师必须在每个学期中选择一些合格的教授参加尊敬的课程。从这个意义上讲，考虑了教学经验，学术培训，竞争等等因素。这项工作通常是由专家（例如教师董事）完成的，这很耗时。到目前为止，已经提出了几种半自动系统来协助头部。在本文中，开发了一个全自动规则的专家系统。拟议的专家系统包括三个主要阶段。首先，将人类专家的知识输入并设计为决策树。在第二步中，根据生成的决策树的提供规则设计了专家系统。在第三步中，提出了一种算法，以根据专家的质量来加重树的结果。为了提高专家系统的性能，开发了多数投票算法，作为后期制作的步骤，以选择满足每门课程最专业决策树的合格培训师。使用伊朗大学的实际数据评估了拟议的专家系统的质量。计算出的准确率为85.55，证明了所提出的系统的鲁棒性和准确性。与相关的有效工作相比，提出的系统几乎没有计算复杂性。另外，简单的实现和透明框是建议系统的其他功能。
Near the entire university faculty directors must select some qualified professors for respected courses in each academic semester. In this sense, factors such as teaching experience, academic training, competition, etc. are considered. This work is usually done by experts, such as faculty directors, which is time consuming. Up to now, several semi-automatic systems have been proposed to assist heads. In this article, a fully automatic rule-based expert system is developed. The proposed expert system consists of three main stages. First, the knowledge of human experts is entered and designed as a decision tree. In the second step, an expert system is designed based on the provided rules of the generated decision tree. In the third step, an algorithm is proposed to weight the results of the tree based on the quality of the experts. To improve the performance of the expert system, a majority voting algorithm is developed as a post-process step to select the qualified trainer who satisfies the most expert decision tree for each course. The quality of the proposed expert system is evaluated using real data from Iranian universities. The calculated accuracy rate is 85.55, demonstrating the robustness and accuracy of the proposed system. The proposed system has little computational complexity compared to related efficient works. Also, simple implementation and transparent box are other features of the proposed system.
