Evaluation of ANN and SVM classifiers as predictors to the diagnosis of students with learning disabilities
Evaluation of ANN and SVM classifiers as predictors to the diagnosis of students with learning disabilities
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评估 ANN 和 SVM 分类器作为学习障碍学生诊断的预测因子
DOI：
10.1016/j.eswa.2007.02.026
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
Ying
中科院分区：
文献类型：
--
作者：
Tung;Shian;Ying
由于学习困难(LD)的内隐特征，学习困难学生的识别和诊断一直是一个难题。学习困难诊断程序通常包括解释一些标准测试或核对表分数，并将它们与统计方法得出的常模进行比较。本文将人工神经网络(ANN)和支持向量机(SVM)两种著名的人工智能技术应用于LD诊断问题。为了提高整体识别精度，我们还实验了基于遗传算法的特征选择算法作为预处理步骤。据我们所知，这是将人工神经网络或支持向量机应用于类似应用的第一次尝试。实验结果表明，人工神经网络在这方面的应用总体上要好于支持向量机，基于包络的遗传算法特征选择过程可以提高LD识别的精度，其中在特征选择过程中使用支持向量机学习器和在分类阶段使用ANN学习器相结合得到的特征集达到了最好的预测精度。最重要的是，研究表明，ANN分类器可以100%的置信度正确识别高达50%的LD学生，这比目前使用的通过统计方法得出的LD诊断预测器要好得多。因此，经过适当训练的神经网络分类模型可以成为LD诊断过程中使用的强有力的预测因子。此外，训练有素的人工神经网络模型也可以用来验证LD诊断程序是否足够。总之，我们期待人工神经网络或支持向量机等人工智能技术在未来的LD诊断应用中发挥重要作用。
Due to the implicit characteristics of learning disabilities (LD), the identification or diagnosis of students with learning disabilities has long been a difficult issue. The LD diagnosis procedure usually involves interpreting some standard tests or checklist scores and comparing them to norms that are derived from statistical method. In this paper, we apply two well-known artificial intelligence techniques, artificial neural network (ANN) and support vector machine (SVM), to the LD diagnosis problem. To improve the overall identification accuracy, we also experiment with GA-based feature selection algorithms as the pre-processing step. To the best of our knowledge, this is the first attempt in applying ANN or SVM to similar application. The experimental results show that ANN in general performs better than SVM in this application, and the wrapper-based GA feature selection procedure can improve the LD identification accuracy, and among all, the combination of using SVM learner in the feature selection procedure and ANN learner in the classification stage results in feature set that achieves the best prediction accuracy. Most important of all, the study indicates that the ANN classifier can correctly identify up to 50% of the LD students with 100% confidence, which is much better than currently used LD diagnosis predictors derived through the statistical method. Consequently, a properly trained ANN classification model can be a strong predictor for use in the LD diagnosis procedure. Furthermore, a well-trained ANN model can also be used to verify whether a LD diagnosis procedure is adequate. In conclusion, we expect that AI techniques like ANN or SVM will certainly play an essential role in future LD diagnosis applications.