不确定性数据的分类方法研究综述

Research on Classification Methods of Uncertain Data

传统的数据挖掘分类方法能够成功地应用于确定性数据分类,但却无法满足绝大多数领域中复杂的不确定性数据的分类需求,由此出现了一系列针对不确定性数据的分类方法。通过大量研究,目前经典的分类算法及针对不确定数据分类的改进方法得到了很大发展,如改进后的支持向量机算法、朴素贝叶斯算法、决策树算法等日渐成熟。

Traditional classification method has a successful application in various fields for the classification of determined knowledge;however,it cannot satisfy the demand for classification of complex uncertain knowledge.Hence,a series of classification methods for uncertain knowledge have emerged.Based on extensive research in this paper,it is proved that at present,classic classification algorithms and improved methods for classifying uncertain data have been greatly developed,such as the improved classification methods about Support Vector Machines,Naive Bayes and Decision Tree.