混合式机器学习系统在家庭服装消费支出决策模型中的应用研究

The Study on Applying Hybrid Machine Learning into Family Apparel Expenditure

混合式机器学习(HML)是在智能信息处理上的一种先进算法,它把以决策树为基础的归纳学习与模块化的神经网络计算结合起来,从而提供了一种在知识基础上进行证实和确认的行之有效的智能化数据挖掘过程。论文将混合式机器学习系统HML进行了全面系统介绍后,将其运用于家庭服装消费支出决策行为的研究中,指出家庭及成员服装消费影响的主要因素是收入、子女性别和季节。在此基础上,选择了家庭服装消费总支出为源数据库,将HML分析所得的结论与前人研究的方法和研究的结果进行了系统比较:从方法上来看,因为属性变量包含间断变量和连续变量两种,因此传统统计分析要运用两种不同检测方法来对影响因素的相关性作出判断,结果需要经过统计学分析,才能得到结论;而HML分析结果比较直观和简单,便于理解;从结果来看,HML的预测精度比传统方法更加精确。

Hybrid Machine Learning （HML） is the latest applying in the field of intelligent information process. It combines the induced-learning-based decision-making tree with the blocking neural network. And it provides a useful intelligent knowledge-based data mining technique. Its core arithmetic is ID3 and FTART. The article introduces the principals of hybrid machine learning firstly, and then applies it into analyzing family apparel expenditures and their influencing factors systematically. Finally, we compare the results from HML with those from the traditional statistic methods. The conclusion is that HML is more friendly and understandable than the traditional methods. Besides, the forecasting by HML is more correct than that by the traditional ways.