Computer Science ›› 2013, Vol. 40 ›› Issue (5): 242-246.

Previous Articles     Next Articles

Bagging-based Probabilistic Neural Network Ensemble Classification Algorithm

JIANG Yun,CHEN Na,MING Li-te,ZHOU Ze-xun,XIE Guo-cheng and CHEN Shan   

  • Online:2018-11-16 Published:2018-11-16

Abstract: Neural networks classification algorithm now more concentrates on the BP algorithm which is the representative of the neural networks.Considering the disadvantage of BP neural network,based on the analysis of probabilistic neural networks and machine learning,and combining with the idea of ensemble learning,we proposed a new classification algorithm which is probabilistic neural networks ensemble based on Bagging.Theoretical analysis and experimental results show that the proposed algorithm can effectively reduce the classification error and improve accuracy of classification.The proposed algorithm has good generalization ability and faster speed of execution than the traditional classification methods such as BP neural networks and it can achieve better and more stable classification result.

Key words: Classification,Back propagation neural network,Probabilistic neural networks,Ensemble learning,Bagging

[1] Phyu T N.Survey of Classification Techniques in Data Mining[C]∥Proceedings of the International MultiConference of Engineers and Computer Scientists.Hong Kong,2009(1)
[2] 罗可,林睦纲,郗东妹.数据挖掘中分类算法综述[J].计算机工程,2009,1(5):3-5
[3] Han Jia-wei,Micheline K.数据挖掘概念与技术(第2版)[M].范明,孟小峰,译.北京:机械工业出版社,2007
[4] El-shafie A,Muklisin M,Najah Ali A,et al.Performance of artificial neural network and regression techniques for rainfall-runoff prediction[J].International Journal of the Physical Sciences,2011,6(8):1997-2003
[5] Selles M A,Schmid S R,Sánchez-Caballero S,et al.Theoretical Model of a Multi-Layered Polymer Coated Steel-Strip Ironing Process Using a Neural Network[C]∥Materials Science Forum.Switzerland,2012:139-144
[6] Jiten P,Choi S-K.Classification approach for reliability-basedtopology optimization using probabilistic neural networks[J].Structural and Multidisciplinary Optimization,2012,5(4):529-543
[7] El-Emary I M,Ramakrishnan S.On the Application of Various Probabilistic Neural Networks in Solving Different Pattern Classification Problems[J].World Applied Sciences Journal,2008,4(6):772-780
[8] Al-Timemy A H,Al-Naima F M,Qaeeb N H.Probabilistic Neural Network for Breast Biopsy Classification[C]∥International Conference on Developments in eSystems Engineering.2009:101-106
[9] Brian P,Stephen M S,Kennedy David N,et al.A Bayesian model of shape and appearance for subcortical brain segmentation[J].NeuroImage,2011,6(3):907-922
[10] Etherm A.机器学习导论[M].范明,昝红英,牛常勇,译.北京:机械工业出版社,2009
[11] Valiant L G.A theory of the learnable[J].Communications of the ACM,1984,7(11):1134-1142
[12] 毕华,梁洪力,王珏.重采样方法与机器学习[J].计算机学报,2009,2(5):862-877
[13] Schapire R E.The strength of weak learnability[J].Machine Learning,1990,5(2):197-227
[14] Robi P.Ensemble learning[EB/OL].http://www.scholarpe-dia.org/article/Ensemble_learning,2012-12-11
[15] Buhlmann P.Bagging, Boosting and Ensemble Methods[M].Berlin:Springer Berlin Heidelberg,2012
[16] Hamid P,Sajad P,Zahra R,et al.CDEBMTE:Creation of Diverse Ensemble Based on Manipulation of Training Example [J].Pattern Recognition,2012,9:197-206
[17] 周志华,陈世福.神经网络集成[J].计算机学报,2002,5(1):1-8
[18] Frank A,Asuncion A.UCI Machine Learning Repository [DB/OL] .http://archive.ics.uci.edu/ml.Irvine,CA:University of California,School of Information and Computer Science,2010
[19] Martis R J,Acharya U R,Tan J H,et al.Application of empirical mode decomposition for automated detection of epilepsy using EEG signals[J].Int J Neural Syst,2012,2(6):1250027
[20] Jayakishan M,Ram B C,Madhab P R,et al.Cascaded Factor Analysis and Wavelet Transform Method for Tumor Classification Using Gene Expression Data[J].International Journal of Information Technology and Computer Science,2012,4:73-79
[21] Adhvaryu P S,Panchal Mahesh P.A Review on Diverse Ensemble Methods for Classification[J].IOSR Journal of Computer Engineering,2012,1(4):27-32
[22] Ye Ren,Suganthan P N.Empirical comparison of bagging-based ensemble classifiers [C]∥Information Fusion,201215th International Conference.2012:917-924
[23] Tian Jin,Li Ming-qiang,Chen Fu-zan,et al.Coevolutionarylearning of neural network ensemble for complex classification tasks[J].Pattern Recognition,2012,5(4):1373-1385

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!