融合SMOTE与Filter-Wrapper的朴素贝叶斯决策树算法及其应用  

Naive Bayesian Decision Tree Algorithm Combining SMOTE and Filter-Wrapper and It's Application

作　　者：许召召 李京华 陈同林 李昕洁 XU Zhao-zhao;LI Ching-hua;CHEN Tong-lin;LEE Shin-jie(School of Software,Yunnan University,Kunming 650091,China;Key Laboratory in Software Engineering of Yunan Province,Kunming 650091,China)

机构地区：[1]云南大学软件学院,昆明650091 [2]云南省软件工程重点实验室,昆明650091

出　　处：《计算机科学》2018年第9期65-69,74,共6页Computer Science

基　　金：国家自然科学基金:云计算环境下双模型驱动面向软件动态演化的建模与分析(61379032)资助

摘　　要：如何对以"工业4.0"为背景的物联网智慧医疗系统所产生的医疗数据进行高效且准确的挖掘仍然是一个十分严峻的问题。而医疗数据往往是高维的、不平衡的和有噪声的,因此提出一种新的数据处理方法——将SMOTE方法与Filter-Wrapper特征选择算法融合,并将其应用于支持临床医疗决策。特别地,所提方法不仅克服了朴素贝叶斯在属性实际应用中因属性独立假设而造成的预测不佳的情况,而且避免了C4.5决策树在构建模型时的过拟合问题。将所提算法应用于ECG临床医疗决策中,取得了很好的效果。How to efficiently and accurately dig out the medical data generated by the Internet-based wisdom medical system with“Industrial 4.0”is still a very serious problem.However,the medical data is often high-dimensional,unba-lanced and noisy,so this paper proposed a new data processing method combining SMOTE method with Filter-Wrapper feature selection algorithm to support clinical decision-making.In particular,the proposed method not only overcomes the situation of bad prediction result of the independent assumptions in the practical attribute application of Naive Bayesian,but also avoids over-fitting problem caused by constructing the model of C4.5 decision tree.What’s more,when the proposed algorithm is applied to ECG clinical decision-making,good results can be obtained.

关 键 词：数据平衡 Wrapper特征选择 朴素贝叶斯 决策树 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]