Multiple decision template classifier combination

Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (27): 216-220.

• 图形、图像、模式识别 • Previous Articles Next Articles

Multiple decision template classifier combination

LIU Ming1，ZHANG Suoliang1，LI Kunlun1，YUAN Baozong2

1.College of Electronic and Information Engineering，Hebei University，Baoding，Hebei 071002，China
2.Institute of Information Science，Beijing Jiaotong University，Beijing 100044，China

Received:1900-01-01 Revised:1900-01-01 Online:2011-09-21 Published:2011-09-21

多决策模板分类器融合

刘明1，张锁良1，李昆仑1，袁保宗2

1.河北大学电子信息工程学院，河北保定 071002
2.北京交通大学信息科学研究所，北京 100044

Abstract

Abstract: A new measurement level classifier combination method is proposed for multi-class problems.This method can decrease the classification error rate by representing each class with multiple decision templates corresponding to all kinds of classification errors.Moreover，in order to accurately calculating the overall confidences related to each class，fuzzy system is employed to describe the relationship between the meta-level samples and the decision templates.This method is tested on three large data sets selected from the public database and is compared with popular classifier combination methods such as voting，Naive Bayesian，linear combination rule，and decision templates.The experimental results show that the method performs well for the classification problems which include 3~15 classes.

Key words: pattern recognition, classifier combination, fuzzy system

摘要： 针对多类分类问题提出了一种新的度量层分类器融合方法，为每个模式类设置多个决策模板，每个决策模板针对一种容易发生的分类错误，从而能够有效地降低错误率;此外，采用模糊系统表示Meta层样本与各个决策模板之间的关系，能够比较准确地计算样本属于各个模式类的总分类置信度。从公用数据仓库中选取了三个较大规模数据集对新方法进行测试，并且与k-近邻规则、投票法、朴素贝叶斯法、线性规则、模板匹配法等常用的分类器融合方法进行了比较。大量实验结果表明，对于类别数在3~15之间的分类问题，该方法具有较好的综合性能。

关键词: 模式识别, 分类器融合, 模糊系统

LIU Ming1，ZHANG Suoliang1，LI Kunlun1，YUAN Baozong2. Multiple decision template classifier combination[J]. Computer Engineering and Applications, 2011, 47(27): 216-220.

刘明1，张锁良1，李昆仑1，袁保宗2. 多决策模板分类器融合[J]. 计算机工程与应用, 2011, 47(27): 216-220.

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Multiple decision template classifier combination

多决策模板分类器融合

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