动态模糊密度的多分类器融合算法

doi:10.3969/j.issn.0372-2112.2018.05.034

电子学报 ›› 2018, Vol. 46 ›› Issue (5): 1246-1252.DOI: 10.3969/j.issn.0372-2112.2018.05.034

动态模糊密度的多分类器融合算法

李艳秋, 任福继, 胡敏

1. 合肥工业大学计算机与信息学院, 安徽合肥 230009;
2. 情感计算与先进智能安徽省重点实验室, 安徽合肥 230009

收稿日期:2017-03-13 修回日期:2017-06-30 出版日期:2018-05-25
- 作者简介:
- 李艳秋 女,1988年生于安徽淮北.合肥工业大学计算机与信息学院在读博士生.研究方向为情感计算、多分类器集成.E-mail:liyanqiu2012@163.com;任福继 男,1959年生于四川南充.合肥工业大学计算机与信息学院教授,博士生导师.研究方向为情感计算、自然语言处理、人工智能等.Email:ren2fuji@gmail.com
- 基金资助:
- 国家自然科学基金 (No.61672202，No.61432004，No.61502141）; 国家自然科学基金-深圳联合基金重点项目 (No.U1613217）

Dynamic Fuzzy Density for Multi-classifier Fusion Algorithm

LI Yan-qiu, REN Fu-ji, HU Min

1. School of Computer and Information, Hefei University of Technology, Hefei, Anhui, 230009, China;
2. Affective Computing and Advanced Intelligent Machines Anhui Key Laboratory, Hefei, Anhui 230009, China

Received:2017-03-13 Revised:2017-06-30 Online:2018-05-25 Published:2018-05-25
- Supported by:
- National Natural Science Foundation of China (No.61672202, No.61432004, No.61502141); Key Program of NSFC-Shenzhen Joint Fund (No.U1613217)

摘要/Abstract

摘要： 在分析现有模糊密度计算方法的基础上，本文从分类器的隶属度分布和输出一致性两方面探索计算模糊密度的新方法，提出一种基于决策信任度和支持度的动态模糊密度赋值方法，旨在根据各分类器识别具体目标时输出的客观信息，实时地刻画分类器在融合系统中的可靠性.在表情识别上的实验结果表明，本文方法可以有效提高模糊积分融合的决策性能，降低单分类器输出不可靠决策信息的干扰，是一种有效的多分类器融合方法.

关键词: 多分类器融合, 模糊积分, 模糊密度, 表情识别

Abstract: Based on the analysis of the existing fuzzy density calculation methods,this paper explores a new method of calculating fuzzy density from the membership degree distribution and output consistency of the classifiers,and proposes a dynamic fuzzy density assignment method based on decision trust and support degree,which aims to describe the reliability of the classifier in the fusion system in real time according to the objective information output when each classifier identifies the specific target.The experimental results on facial expression recognition show that the proposed method can effectively improve the decision performance of fuzzy integral fusion and reduce the interference of unreliable decision information output by single classifier,which is an effective multi-classifier fusion method.

Key words: multi-classifier fusion, fuzzy integral, fuzzy density, facial expression recognition

中图分类号:

TP391

李艳秋, 任福继, 胡敏. 动态模糊密度的多分类器融合算法[J]. 电子学报, 2018, 46(5): 1246-1252.

LI Yan-qiu, REN Fu-ji, HU Min . Dynamic Fuzzy Density for Multi-classifier Fusion Algorithm[J]. Acta Electronica Sinica, 2018, 46(5): 1246-1252.

参考文献

[1] Bejani M,Gharavian D,Charkari N M.Audiovisual emotion recognition using ANOVA feature selection method and multi-classifier neural networks[J].Neural Computing and Applications,2014,24(2):399-412.
[2] Huang Z H,Li W J,Wang J,et al.Face recognition based on pixel-level and feature-level fusion of the top-level's wavelet sub-bands[J].Information Fusion,2015,22:95-104.
[3] Eleftheriadis S,Rudovic O,Pantic M.Discriminative shared Gaussian processes for multiview and view-invariant facial expression recognition[J].IEEE Transactions on Image Processing,2014,24(1):189-204.
[4] Cho S B,Kim J H.Combining multiple neural network by fuzzy integral for robust classification[J].IEEE Transactions on Systems Man & Cybernetics,1995,25(2):380-384.
[5] Kwak K C,Pedrycz W.Face recognition:A study in information fusion using fuzzy integral[J].Pattern Recognition Letters,2005,26(6):719-733.
[6] Claudio De Stefano,Ciro D'elia,et al.Classifier combination by Bayesian networks for handwriting recognition[J].International Journal of Pattern Recognition & Artificial Intelligence,2009,23(5):887-905.
[7] Guo K,Li W.Combination rule of D-S evidence theory based on the strategy of cross merging between evidences[J].Expert Systems with Applications,2011,38(10):13360-13366.
[8] 林剑,鲍光淑,王润生,等.基于模糊密度分解的遥感图像光谱和纹理信息的融合[J].电子学报,2004,32(12):2028-2030. Lin Jian,Bao Guangshu,Wang Yusheng,et,al.Fusion spectrum and texture information of RS image based on decomposing fuzzy density[J].Acta Electronica Sinica,2004,32(12):2028-2030.(in Chinese)
[9] 孔志周,蔡自兴,官东.两种模糊密度确定方法的实验比较[J].小型微型计算机系统,2009,30(2):283-288. Kong Zhizhou,Cai Zixing,Guan Dong.Empirical comparison of two methods for fuzzy density[J].Journal of Chinese Computer Systems,2009,30(2):283-288.(in Chinese)
[10] 刘汝杰,袁保宗,唐晓芳.用遗传算法实现模糊测度赋值的一种多分类器融合算法[J].电子学报,2002,30(1):145-147. Liu Rujie,Yuan Baozong,Tang Xiaofang.Multiple classifiers fusion algorithm with the fuzzy measures determined by genetic algorithm[J].Acta Electronica Sinica,2002,30(1):145-147.(in Chinese)
[11] 付耀文,黎湘,庄钊文.一种自适应模糊密度赋值的决策层融合目标识别算法[J].电子学报,2004,32(9):1433-1435. Fu Yaowen,Li Xiang,Zhuang Zhaowen.A decision level fusion algorithm for target recognition with fuzzy densities determined adaptively[J].Acta Electronica Sinica,2004,32(9):1433-1435.(in Chinese)
[12] 邢清华,刘付显.一种基于模糊密度动态调节的融合目标识别方法[J].控制与决策,2009,24(5):777-780. Xing Qinghua,Liu Fuxian.Method of fusion target recognition based on fuzzy densities dynamic adjusted[J].Control and Decision,2009,24(5):777-780.(in Chinese)
[13] 孔志周.多分类器系统中信息融合方法研究[D].中南大学,2011. Kong Zhizhou.Study of information fusion methods in multiple classifier system[D].Central South University,2011.(in Chinese)
[14] 詹永照,张娟,毛启容.基于可分度和支持度的模糊密度赋值融合识别算法[J].模式识别与人工智能,2012,25(2):346-351. Zhan Yongzhao,Zhang Juan,Mao Qirong.Fusion recognition algorithm based on fuzzy density determination with classification capability and supportability[J].PR&AI,2012,25(2):346-351.(in Chinese)
[15] 陈鼎新,陆文凯,刘代志.向量距离中角度信息对时空Kriging的影响[J].清华大学学报(自然科学版),2016,56(5):553-557. Chen Dingxin,Lu Wencai,Liu Dai-zhi.Vector distance direction information for spatio-temporal Kriging[J].J Tsinghua Univ(Sci&Technol),2016,56(5):553-557.(in Chinese)
[16] 印勇,李荣岗,王建东,等.人脸表情识别的Gabor变换最优通道模糊融合方法[J].重庆大学学报,2010,33(7):97-101. Yin Yong,Li Ronggang,Wang Jian-Dong,et al.A fuzzy fusion approach to gabor transform optimal channels for facial expression recognition[J].Journal of Chongqing University,2010,33(7):97-101.(in Chinese)
[17] Gu W,Xiang C,Venkatesh Y V,et al.Facial expression recognition using radial encoding of local Gabor features and classifier synthesis[J].Pattern Recognition,2012,45(1):80-91.
[18] Wang L,Wang K,Li R.Unsupervised feature selection based on spectral regression from manifold learning for facial expression recognition[J].Iet Computer Vision,2015,9(5):655-662.
[19] Lv Y,Feng Z,Xu C.Facial expression recognition via deep learning[A].International Conference on Smart Computing[C].IEEE,2014.303-308.
[20] 胡敏,江河,王晓华,等.基于几何和纹理特征的表情层级分类方法[J].电子学报,2017,45(1):164-172. Hu Min,Jiang He,Wang Xiaohua,et al.A hierarchical classification method of expressions based on geometric and texture features[J].Acta Electronica Sinica,2017,45(1):164-172.(in Chinese)
[21] Zhang W,Zhang Y,Ma L,et al.Multimodal learning for facial expression recognition[J].Pattern Recognition,2015,48(10):3191-3202.
[22] Yang M,Zhang L,Zhang L,et al.Monogenic Binary Pattern (MBP):A Novel Feature Extraction and Representation Model for Face Recognition[A].International Conference on Pattern Recognition[C].IEEE Computer Society,2010.2680-2683.
[23] Hu M,Huang X,Wang X,et al.Adaptive facial expression recognition method based on MBP and HMOG feature[A].International Conference on Cloud Computing and Intelligence Systems[C].IEEE,2014.
[24] 黄晓音.基于单演示波和稀疏表示的人脸表情识别方法[D].合肥工业大学,2016. Huang Xiaoyin.Facial expression recognition method based on monogenic wave and sparse representation[D].Hefei University of Technology,2016.(in Chinese)
[25] Qin W,Fang Q,Yang Y.A facial expression recognition method based on singular value features and improved bp neural network[J].Communications in Computer & Information Science,2013,363:163-172.
[26] Khanum A,Mufti M,Javed M Y,et al.Fuzzy case-based reasoning for facial expression recognition[J].Fuzzy sets and systems,2009,160(2):231-250.

动态模糊密度的多分类器融合算法

Dynamic Fuzzy Density for Multi-classifier Fusion Algorithm

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[12]	林剑, 鲍光淑, 王润生, 王欣. 基于模糊密度分解的遥感图像光谱和纹理信息的融合[J]. 电子学报, 2004, 32(12): 2028-2030.
[13]	刘汝杰;袁保宗;唐晓芳. 用遗传算法实现模糊测度赋值的一种多分类器融合算法[J]. 电子学报, 2002, 30(1): 145-147.
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