受限玻尔兹曼机在信息学专业选择中的实现

Vinna Rahmayanti Setyaning Nastiti, Zamah Sari, Bella Chintia Eka Merita
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引用次数: 0

摘要

对一些学生来说,选择专业并不是一件容易的事,尤其是对那些对自己的技能和能力缺乏信心的学生。高等教育的专业化成为学生未来事业成功的基准和关键。本研究利用2013-2015届已毕业学生的数据,提供信息学专业学生专业分类的学习成果记录。总数据为319名学生。本研究使用的分类方法是受限玻尔兹曼机(RBM)。然而,由于每个领域的数量差异很大,数据显示班级分布不平衡。因此,加入SMOTE对不平衡类进行分类。RBM和SMOTE联合使用的准确率为70%,均方误差为0.4。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Implementation of Restricted Boltzmann Machine in Choosing a Specialization for Informatics Students
Choosing a specialization was not an easy task for some students, especially for those who lacked confidence in their skill and ability. Specialization in tertiary education became the benchmark and key to success for students’ future careers. This study was conducted to provide the learning outcomes record, which showed the specialization classification for the Informatics students by using the data from the students of 2013-2015 who had graduated. The total data was 319 students. The classification method used for this study was the Restricted Boltzmann Machine (RBM). However, the data showed imbalanced class distribution because the number of each field differed greatly. Therefore, SMOTE was added to classify the imbalanced class. The accuracy obtained from the combination of RBM and SMOTE was 70% with a 0.4 mean squared error.
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