基于模糊规则的多分类器融合

刘明; 袁保宗; 苗振江; 唐晓芳

doi:10.3724/SP.J.1146.2005.01587

基于模糊规则的多分类器融合

doi: 10.3724/SP.J.1146.2005.01587

基金项目:

国家973计划(2004CB318110)，国家自然科学主任基金(60441002)和大学重大项目基金( 2003SZ002)资助课题

计量
- 文章访问数: 3501
- HTML全文浏览量: 100
- PDF下载量: 1244
- 被引次数: 0
出版历程
- 收稿日期: 2005-12-08
- 修回日期: 2006-07-06
- 刊出日期: 2007-07-19

Fuzzy Rule-Based Multiple Classifier Fusion

摘要

摘要: 用非线性方法解决多分类器融合问题能够取得比较高的识别率，但是，当前被应用在多分类器融合领域中的非线性方法可理解性较差，给使用者带来一定的困难。而基于模糊规则的模式识别方法是一类可理解性好的非线性方法，但迄今为止还没有被应用于多分类器融合问题中。基于上述考虑，该文将模糊系统应用到多分类器融合中，并且研究了如何设计可理解性好、精度高的模糊系统的问题，提出了一种改进的基于支持向量的模糊系统设计方法。该方法在从ELENA项目数据库和UCI数据库中选出的4个数据集上进行了测试。实验结果表明，该方法能够用可理解性好的模糊系统实现低错误率的多分类器融合。
- 信息融合;模式识别;模糊逻辑;支持向量机
Abstract: Nonlinear methods perform well in the multiple classifier fusion. However, the nonlinear methods used for the multiple classifier fusion have poor comprehensibility. As a nonlinear method, the fuzzy rule-based pattern recognition has good comprehensibility, but has not been applied to the multiple classifier fusion. Therefore, this paper introduces fuzzy system to the classifier fusion, where the designing issues for accurate and comprehensible fuzzy system are studied, and an improved support vector based fuzzy rule system designing method is proposed. Experiments have been carried out on four data sets from the ELENA project database and the UCI database. The experimental results show that the proposed method can fuse multiple classifiers with low classification error rate based on comprehensible fuzzy systems.

HTML全文

参考文献(1)

Kimura F and Shridhar M. Handwritten numerical recognition based on multiple algorithms[J].Pattern Recognition.1991, 24(10):969-983[2]Rogova G. Combining the results of several neural network classifiers[J].Neural Networks.1994, 7(5):777-781[3]Keller J M, Gader P, and Tahani H, et al.. Advances in fuzzy integration for pattern recognition[J].Fuzzy Sets and Systems.1994, 65(2/3):273-283[4]Lee D S and Srihari S N. A theory of classifier combination: The neural network approach[J].Proceedings of the Third International Conference on Document Analysis and Recognition. Montreal, Canada.1995,1:42-45[5]Ting K M and Witten I H. Issues in stacked generalization. Journal of Artificial Intelligence Research, 1999, 10(5): 271- 289.[6]Toh K A. A reduced multivariate polynomial model for multimodal biometrics and classifiers fusion. IEEE Trans. on Systems, Man and Cybernetics, 2004, 14(2): 224-233.[7]Huang Y S and Suen C Y. A method of combining multiple classifiers-a neural network approach. Proceedings of the 12th IAPR International Conference on Pattern Recognition. Jerusalem, Palestine. 1994, 2: 473-475.[8]Wang H L, Kwong S, Jin Y C, Wei W, and Man K F. Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction[J].Fuzzy Sets and Systems.2005, 149(1):149-186[9]Ishibuchi H, Nakashima T, and Murata T. Three-objective genetic-based machine learning for linguistic rule extraction[J].Information Science.2001, 136(1-4):109-133[10]Yin T K. Characteristic-point-based fuzzy inference system aimed to minimize the number of fuzzy rules[J].IEEE Trans. on Fuzzy System.2004, 12(2):250-273[11]Chen Y H and Wang J Z. Support vector learning for fuzzy rule-based classification system[J].IEEE Trans. on Fuzzy System.2003, 11(6):716-728[12]Mitra P, Murthy C A, and Sankar K P. Density-based multiscale data condensation[J].IEEE Trans. on Pattern Analysis and Machine Intelligence.2002, 24(6):734-747[13]Wang L X and Mendel J M. Fuzzy basis functions, universal approximation, and orthogonal least-squares learning[J].IEEE Trans. on Neural Networks.1992, 3(5):807-814[14]Berg J V D, Kaymak U, and Willem V D B. Fuzzy classification using probability based rule weighting. Proc. 11th IEEE Int.Conf. Fuzzy Systems. Honolulu, HI, America. May 2002: 991-996.[15]Platt J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers. Smola A, et al.. MIT Press. 1999: 61-74.[16]Duin R P W and Tax D M J. Classifier conditional posterior probabilities. In: Advances in Pattern Recognition. Amin A, Dori D and Freeman H Eds.. Lecture Notes in Computer Science 1451. Springer, Berlin. 1998: 611-619.[17]Ho T K. Nearest neighbours in random subspaces. Proceedings of the Second International Workshop on Statistical Techniques in Pattern Recognition. Sydney, Australia. 1998: 640-649.

施引文献

资源附件(0)

访问统计