• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>International Journal of Engineering & Technology >Exploring Subjective Well-Being Factors with Support Vector Machine
【24h】

Exploring Subjective Well-Being Factors with Support Vector Machine

机译：用支持向量机探索主观幸福感

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

National-level Subjective well-being (SWB) is known to be associated with six traditional factors, that is, GDP per capita, social support, healthy life expectancy, social freedom, generosity, and absence of corruption, but debates persist about the variability of these six factors. Whether the predicting in SWB is based only on these six factors or not? Are there any country-specific factors? Thus, we examined these two questions with the data sets from World Happiness Report and OECD database for U.S.. By control-ling the other factors except only one, we employed Support Vector Machine (SVM) to identify the weight of each factor without worrying about the limitations caused by the small size of the sample in years. We found that another three factors (protein con-sumption, fruit consumption and physician ratio among all employee) in addition to the six traditional factors can also affect na-tional-level SWB in U.S.; Moreover, the power of SVW in prediction is as high as 95.08%, which is much greater than that present-ed by the linear regression models (74.3%).
机译：众所周知，国家一级的主观幸福感与六个传统因素有关，即人均GDP，社会支持，健康的预期寿命，社会自由，慷慨和没有腐败，但关于可变性的争论仍在继续这六个因素中的一个。 SWB中的预测是否仅基于这六个因素？是否有特定国家/地区的因素？因此，我们使用世界幸福报告和美国OECD数据库中的数据集研究了这两个问题，通过控制除一个因素外的其他因素，我们使用支持向量机（SVM）来确定每个因素的权重而无需担心数年之内样本量过小造成的局限性。我们发现，除了六个传统因素外，另外三个因素（蛋白质消耗，水果消耗和所有员工中的医师比例）也会影响美国的国家SWB;此外，SVW在预测中的功效高达95.08％，远高于线性回归模型所显示的力量（74.3％）。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号