HML系统在商店偏好决策模型中的应用研究

The Study on Applying Hybrid Machine Learning into Store Choice Preference

对混合式机器学习系统(HML)进行了全面系统的介绍,并将其运用于服装消费商店偏好决策行为的研究中。采用上海统计局家庭调查网络,对300户家庭进行抽样分析后发现,服装消费商店偏好的主要影响因素是地区、季节、丈夫和妻子的学历、职业以及子女性别。在此基础上,将HML分析所得的结论与传统的研究方法和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的检测方法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。

Hybrid Machine Learning （HML） is the latest applying in the field of intelligent iformation process. It combines the induced learning based - on 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 introduced the principals of hybrid machine learning firstly, and then applied it into analyzing store choice preference and their influencing factors systematically. Finally, the author compared the results from HML with those from the traditional statistic methods. The conclusion is that HML is more friendly and easily to be understood than the traditional methods.