计算机工程与应用2017，Vol.53Issue(5)：45-50,146,7.DOI:10.3778/j.issn.1002-8331.1507-0210
基于相关性的类偏好敏感决策树算法
Novel class preference sensitive decision tree algorithm based on correlation
摘要
Abstract
In view of the fact that decision makers will have preference to one certain result when in the face of several classification results, it proposes a Class Preference Sensitive Decision Tree algorithm based on correlation(CPSDT). The algorithm introduces the concept of class-preference, the degree of class-preference and the preference cost matrix. To make up for the weakness that the correlations between non-class attributes are not considered when choosing the splitting at-tribute in the traditional decision tree constructing process, the algorithm uses the features pre-screened based on the correlation to exclude the redundant attributes before learning and reconstructs the attribute selection fator which is based on correlation. The experimental results show that this algorithm can reduce the size of the decision tree effectively. Further more, the algorithm can not only achieve the high precision prediction of preference class, but also can ensure the decision tree has good overall accuracy.关键词
分类/决策树/属性选择因子/偏好敏感Key words
classification/decision tree/attribute selection factor/preference sensitive分类
信息技术与安全科学引用本文复制引用
周美琴,徐章艳,陈诗旭,李艳红,马顺,展雪梅..基于相关性的类偏好敏感决策树算法[J].计算机工程与应用,2017,53(5):45-50,146,7.基金项目
国家自然科学基金(No.61462010,No.61363036,No.61262004) （No.61462010,No.61363036,No.61262004）
广西"多源信息挖掘与安全"重点实验室主任基金 （）
广西自然科学基金(No.2011GXNSFA018163). （No.2011GXNSFA018163）
