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Detection of IAD based on personality questionnaires of Chinese college students and SVMs

机译:基于中国大学生人格问卷和支持向量机的IAD检测

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The unprecedented development of the Internet brings many benefits but it also manifests the Internet addiction disorder (IAD) which has a huge impact on some people in our society. However, it is hard to diagnose and prevent IAD. In this study, we propose a new way to detect IAD using C-SVM and V-SVM by inviting 580 Chinese College Students to complete the questionnaires which consist of Brief Self Control Scale (BSCS), the 11th version of Barratt Impulsiveness Scale (BIS-11), Chinese Big Five (CBF) and Chen Internet Addiction Scale (CIAS). BSCS, BIS-11 and CBF evaluate one's personality in nine subscales. CIAS score is used to label the high and low group. We compare the performances of two SVMs on two datasets with different features by 5-fold cross validation after normalization. It is found that IAD could be detected from personality questionnaire data using SVM effectively. In particular, C-SVM with RBF function on the dataset with only subscales normalized in the range of [-1,1] was preferred.
机译:互联网的空前发展带来了许多好处,但同时也表明互联网成瘾症(IAD)对我们社会中的某些人产生了巨大影响。但是,很难诊断和预防IAD。在这项研究中,我们通过邀请580名中国大学生填写问卷,提出了一种使用C-SVM和V-SVM检测IAD的新方法,该问卷包括简短自我控制量表(BSCS),第11版Barratt冲动量表(BIS) -11),中国大五(CBF)和陈互联网成瘾量表(CIAS)。 BSCS,BIS-11和CBF在9个分量表中评估一个人的性格。 CIAS分数用于标记高和低组。通过归一化后的5倍交叉验证,我们比较了两个支持向量机在具有不同特征的两个数据集上的性能。研究发现,使用支持向量机可以有效地从人格问卷数据中检测出IAD。特别是,在数据集上仅具有在[-1,1]范围内归一化的子尺度的具有RBF功能的C-SVM是首选。

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