教育数据挖掘:选课过程中的分类器比较

S. Srivastava, Saif Karigar, R. Khanna, R. Agarwal
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引用次数: 7

摘要

教育系统在印度和世界各地显示水平转变,而不是垂直发展的一个特定的领域。当前情景下的工科学生努力从各种跨学科课程中积累知识,并在各自的研究领域中发展应用[2]-[4]。这种跨学科的增长也可以使用各种数据挖掘技术来支持和比较未来的预测,并为当前课程的选择提供数学基础。本文着重介绍了一所著名私立大学的公开选修课的选择研究。数据挖掘过程回顾、应用和比较了K-NN、径向基核支持向量机等分类算法。本文还旨在采用数据挖掘技术作为迄今为止使用的启发式过程的数学基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Educational Data Mining: Classifier Comparison for the Course Selection Process
The education system in India & across the world has shown a horizontal shift instead of vertical development in one specific domain. The Engineering student in current scenario try to accumulate knowledge from various interdisciplinary course’s and develop application in respective area of study [2]–[4]. This interdisciplinary growth can also be supported and compared using various data mining techniques for future prediction and provide a mathematical foundation for the current selection of the course. This paper emphasis on one such study done for opting the open elective course at leading private university. The data mining process review, apply and compare the classification algorithms like K-NN, Support Vector machine with radial basis kernel. The paper also aims at adopting the data mining techniques as the mathematical foundation for the heuristic process being used till date.
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