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首页> 外文会议>International Conference on Cybernetics and Information Technologies, Systems and Applications(CITSA2004) and 10th International Conference on Information Systems Analysis and Synthesis(ISAS2004) vol.1 >Mining Feature Importance: Applying Evolutionary Algorithms within a Web-based Educational System
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Mining Feature Importance: Applying Evolutionary Algorithms within a Web-based Educational System

机译：挖掘特征的重要性：在基于Web的教育系统中应用进化算法

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摘要

A key objective of data mining is to uncover the hidden relationships among the objects in given data set. Classification has emerged as a popular task to find the groups of similar objects in order to predict unseen test data points. Classification fusion combines multiple classifications of data into a single classification solution of higher accuracy. Feature extraction aims to reduce the computational cost of feature measurement, increase classifier efficiency, and allow greater classification accuracy based on the process of deriving new features from the original features. Recently web-based educational systems collect vast amounts of data on user patterns, and data mining methods can be applied to these databases to discover interesting associations based on students' features, problems' attributes, and the actions taken by students in solving homework and exam problems. This paper represents an approach for classifying students in order to predict their final grades based on features extracted from logged data in an educational web-based system. By weighing feature vectors representing feature importance using a Genetic Algorithm we can optimize the prediction accuracy and obtain significant improvement over raw classification. This work represents a rigorous application of known classifiers as a means of analyzing and comparing use and performance of students who have taken a technical course that was partially/completely administered via the web.
机译：数据挖掘的关键目标是发现给定数据集中对象之间的隐藏关系。分类已成为查找相似对象组以预测看不见的测试数据点的流行任务。分类融合将数据的多个分类组合到一个更高精度的单个分类解决方案中。特征提取旨在降低特征量度的计算成本，提高分类器效率，并基于从原始特征中派生新特征的过程来提高分类精度。最近基于网络的教育系统收集了大量有关用户模式的数据，并且可以将数据挖掘方法应用于这些数据库，以根据学生的特征，问题的属性以及学生在解决作业和考试中所采取的行动来发现有趣的关联。问题。本文提出了一种对学生进行分类的方法，以便根据从基于教育的网络系统中记录的数据中提取的特征来预测其最终成绩。通过使用遗传算法对表示特征重要性的特征向量进行加权，我们可以优化预测精度并获得比原始分类明显的改进。这项工作代表了对已知分类器的严格应用，作为分析和比较已通过网络部分/完全管理的技术课程的学生的使用和表现的一种手段。

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