基于直觉模糊核匹配追踪集成的目标识别方法

doi:10.11959/j.issn.1000-436x.2015260

Abstract

Abstract:

Considering that the generalization of the learning machine performed poorly in the present intuitionistic fuzzy kernel matching pursuit algorithm(IFKMP)due to its training method and stopping criteria,a new recognition method based on intuitionistic fuzzy kernel matching pursuit ensemble(IFKMPE)was proposed by introducing the idea of ensemble learning.In IFKMPE,the double perturbation strategy including sample and parameter perturbation was applied to generate the sub-learning machine,the recognition results were fused by the principle of majority voting,and therefore both the classify accuracy and generation ability were enhanced.Simulation results show the new algorithm IFKMPE performs better in terms of recognition accuracy and stability of sample learning compared with the traditional ones.

Key words: intuitionistic fuzzy set, kernel matching pursuit, ensemble learning, target recognition

Xiao-dong YU,Ying-jie LEI,Ya-fei SONG,Shao-hua YUE,Jun-hong HU. Intuitionistic fuzzy kernel matching pursuit ensemble based target recognition[J]. Journal on Communications, 2015, 36(10): 165-171.

Figures/Tables 7

References 5

[6]	李青, 焦李成, 周伟达 . 基于模糊核匹配追寻的特征模式识别[J]. 计算机学报, 2009,32(8): 1687-1694. LI Q , JIAO L C , ZHOU W D . Pattern recognition based on the fuzzy kernel matching pursuit[J]. Chinese Journal of Computers, 2009,32(8): 1687-1694.
[7]	雷阳, 雷英杰, 周创明 . 基于直觉模糊核匹配追踪的目标识别方法[J]. 电子学报, 2011,39(6): 1441-1446. LEI Y , LEI Y J , ZHOU C M . Techniques for target recognition based on intuitionistic fuzzy kernel matching pursuit[J]. Acta Electronica Sinica, 2011,39(6): 1441-1446.
[8]	HANSEN L K , SALAMON P . Neural network ensemble[J]. IEEE Transactions on Pattern analysis and Machine Intelligence, 1990,12(10): 993-1001.
[9]	SCHAPIRE R E . The strength of weak learn ability[J]. Machine Learning, 1990,5(2): 197-227.
[10]	BARTOSZ K,MICHA? W,BOGUS?AW C . Clustering-based ensembles for one-class classification[J]. Information Sciences, 2014,264(Complete): 182-195.
[11]	MONTHER A , WANG D H . Fast decorrelated neural network ensembles with random weights[J]. Information Sciences, 2014,264: 104-117.
[12]	KIM H C , PAN S N . Constructing support vector machine ensemble[J]. Pattern Recognition, 2003,36(12): 2757-2767.
[13]	JIAO L C , LI Q . Kernel matching pursuit classifier ensemble[J]. Pattern Recognition, 2006,39(4): 587-594.
[14]	MORDELET F , VERT J P . A bagging SVM to learn from positive and unlabeled examples[J]. Pattern Recognition Letters, 2014,37: 201-209.
[15]	YANG K H , ZHAO L L . A new optimizing parameter approach of LSSVM multiclass classification model[J]. Neural Computing ＆ Applications, 2012,21(5): 954-955.
[1]	PASCAL V , BENGIO Y . Kernel matching pursuit[J]. Machine Learning, 2002,48(1-3): 165-187.
[2]	CEVHERV , KRAUSE A . Greedy dictionary selection for sparse representation[J]. IEEE Journal of Selected Topics Signal Processing, 2011,5(5): 979-988.
[3]	SUN P , YAO X . Sparse approximation through boosting for learning large scale kernel machines[J]. IEEE Transaction on Neural Networks, 2010,21(6): 883-894.
[4]	付丽华, 李宏伟, 张猛 . 基于更贪心策略的快速正交核匹配追踪算法[J]. 电子学报, 2013,41(8): 1580-1585. FU L H , LI H W , ZHANG M . Fast orthogonal kernel matching pursuit based on greedier strategy[J]. Acta Electronica Sinica, 2013,41(8): 1580-1585.
[16]	王晓丹, 孙东延, 郑春颖 ,等. 一种基于AdaBoost的SVM分类器[J]. 空军工程大学学报(自然科学版), 2006,7(6): 54-57. WANG X D , SUN D Y , ZHENG C Y ,et al. A combined SVM classifier based on AdaBoost[J]. Journal of Air Force Engineering University(Natural Science Edition), 2006,7(6): 54-57.
[5]	雷阳, 孔韦韦, 雷英杰 . 基于直觉模糊 c 均值聚类核匹配追踪的弹道中段目标识别方法[J]. 通信学报, 2012,33(11): 136-143. LEI Y , KONG W W , LEI Y J . Technique for target recognition based on intuitionistic fuzzy c-means clustering and kernel matching pursuit[J]. Journal on Communications, 2012,33(11): 136-143.

Metrics

Recommended 0

No Suggested Reading articles found!

算法	训练规模	测试规模	集成规模	训练时间/s	识别率/%	偏差/%
KMP			1	2.863	75.58	2.21
KMPE			15	42.946	78.69	0.79
IFKMP	400	+:500	1	4.443	93.24	2.17
Bagging-IFKMPE			15	68.199	1	0
ES-IFKMPE			15	65.692	1	0

算法	训练规模	测试规模	集成规模	训练时间/s	识别率/%	偏差/%
KMP	500		1	5.825	85.42	1.11
KMPE	500		25	109.26	89.59	0.69
IFKMP	500	+:500	1	6.474	93.36	1.02
Bagging-IFKMPE	200		15	31.994	97.72	0.34
ES-IFKMPE	200		15	31.238	97.50	0.38

算法	训练规模	测试规模	集成规模	训练时间/s	识别率/%	偏差/%
KMP	300		1	1.357 8	67.63	5.68
KMPE	300		5	6.236	70.59	1.36
IFKMP	300	+:150	1	1.421 1	92.77	6.06
Bagging-IFKMPE	60		5	0.630 2	96.04	2.21
ES-IFKMPE	60		5	0.644 5	97.32	0.89

Intuitionistic fuzzy kernel matching pursuit ensemble based target recognition

RichHTML

PDF

Knowledge

Cited

Abstract

Cite this article

share this article

Figures/Tables 7

References 5

Related Articles 7

Metrics

Recommended 0

[1]	Qianyi DAI, Bin ZHANG, Song GUO, Kaiyong XU. Blockchain network layer anomaly traffic detection method based on multiple classifier integration [J]. Journal on Communications, 2023, 44(3): 66-80.
[2]	Zunwen HE, Shuai HOU, Wancheng ZHANG, Yan ZHANG. Multi-feature fusion classification method for communication specific emitter identification [J]. Journal on Communications, 2021, 42(2): 103-112.
[3]	Xiaochuan SUN,Zhigang LI,Minghui ZHANG,Guan GUI. Multi-channel QoT prediction method in wide-area optical backbone network based on ensemble learning [J]. Journal on Communications, 2020, 41(9): 1-7.
[4]	Ying CUI,Kai XU,Zhongjun LU,Shubin LIU,Liguo WANG. Combination strategy of active learning for hyperspectral images classification [J]. Journal on Communications, 2018, 39(4): 91-99.
[5]	Xiao-shi FAN,Ying-jie LEI,Yan-li LU,Ya-nan WANG. Long-term intuitionistic fuzzy time series forecasting model based on DTW [J]. Journal on Communications, 2016, 37(8): 95-104.
[6]	UXiao-dong Y,EIYing-jie L,UEShao-hua Y,ANGRui W. Research on PSO-based intuitionistic fuzzy kernel clustering algorithm [J]. Journal on Communications, 2015, 36(5): 74-80.
[7]	Yang LEI,Wei-wei KONG,Ying-jie LEI. Technique for target recognition based on intuitionistic fuzzy c-means clustering and kernel matching pursuit [J]. Journal on Communications, 2012, 33(11): 136-143.