[1]ZHANG Xiongtao,HU Wenjun,WANG Shitong.Ensemble deep belief network based on fuzzy partitioning and fuzzy weighting[J].CAAI Transactions on Intelligent Systems,2019,14(5):905-914.[doi:10.11992/tis.201809018]
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Ensemble deep belief network based on fuzzy partitioning and fuzzy weighting

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 5 Page number: 905-914 Column: 学术论文—人工智能基础 Public date: 2019-09-05
Title:
Ensemble deep belief network based on fuzzy partitioning and fuzzy weighting
Author(s):
ZHANG Xiongtao1; 2;  HU Wenjun2;  WANG Shitong1
1. School of Digital Media, Jiangnan University, Wuxi 214122, China;
2. School of Information Engineering, Huzhou University, Huzhou 313000, China
Keywords:
ensemble; deep belief network; fuzzy partition; fuzzy weighting; running time; fuzzy clustering algorithm (FCM); fuzzy theory
CLC:
O235;TP18
DOI:
10.11992/tis.201809018
Abstract:
Aiming at the problems of high training time complexity and easy over-fitting of the deep belief network (DBN) algorithm, inspired by the fuzzy theory, an ensemble deep belief network based on fuzzy partitioning and fuzzy weighting, namely FE-DBN (ensemble deep belief network with fuzzy partition and fuzzy weighting), is proposed to deal with the classification of large-scale data. First, the training data is divided into several subsets by fuzzy clustering algorithm (FCM), and then the DBNs of different structures are trained in parallel on each subset. Finally, the results of each classifier are ensembled by fuzzy weighting. Experiments on artificial datasets and UCI datasets show that the proposed FE-DBN outperforms the DBN in terms of accuracy and running time.
References:
[1] HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786):504-507.
[2] HINTON G E. A practical guide to training restricted boltzmann machines[M]//MONTAVON G, ORR G B, MüLLER K R. Neural Networks:Tricks of the Trade. Berlin, Heidelberg:Springer, 2012:599?619.
[3] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural computation, 2006, 18(7):1527-1544.
[4] DESJARDINS G, COURVILLE A, BENGIO Y. Adaptive parallel tempering for stochastic maximum likelihood learning of RBMs[C]//Proceedings of NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning. Granada, Spain, 2010.
[5] LEE S, CHANG J H. Deep belief networks ensemble for blood pressure estimation[J]. IEEE access, 2017, 5:9962-9972.
[6] QIU Xueheng, ZHANG Le, REN Ye, et al. Ensemble deep learning for regression and time series forecasting[C]//Proceedings of 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning. Orlando, USA, 2014:1?6.
[7] ZHANG Chong, LIM P, QIN A K, et al. Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics[J]. IEEE transactions on neural networks and learning systems, 2017, 28(10):2306-2318.
[8] MOHAMED A R, YU Dong, DENG Li. Investigation of full-sequence training of deep belief networks for speech recognition[C]//Proceedings of Interspeech 2010. Chiba, Japan, 2010:2846-2849.
[9] AU W H, CHAN K C C, WONG A K C. A fuzzy approach to partitioning continuous attributes for classification[J]. IEEE transactions on knowledge and data engineering, 2006, 18(5):715-719.
[10] CHUANG C C. Fuzzy weighted support vector regression with a fuzzy partition[J]. IEEE transactions on systems, man, and cybernetics, part B, 2007, 37(3):630-640.
[11] RATHORE P, BEZDEK J C, ERFANI S M, et al. Ensemble fuzzy clustering using cumulative aggregation on random projections[J]. IEEE transactions on fuzzy systems, 2018, 26(3):1510-1524.
[12] YU Zhiwen, ZHANG Yidong, CHEN C L P, et al. Multiobjective semisupervised classifier ensemble[J]. IEEE transactions on cybernetics, 2019, 49(6):2280-2293.
[13] ZHOU Ta, ZHANG Zhifeng, WANG Sifan, et al. Stacked unitwise fast combination classification for single hidden layer neural networks using extreme learning machine on EEG signals[J]. Journal of medical imaging and health informatics, 2018, 8(7):1491-1495.
[14] KUNCHEVA L I, WHITAKER C J. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy[J]. Machine learning, 2003, 51(2):181-207.
[15] KITTLER J, HATEF M, DUIN R P W, et al. On combining classifiers[J]. IEEE transactions on pattern analysis and machine intelligence, 1998, 20(3):226-239.
[16] 蒋亦樟, 朱丽, 刘丽, 等. 多视角模糊双加权可能性聚类算法[J]. 智能系统学报, 2017, 12(6):806-815 JIANG Yizhang, ZHU Li, LIU Li, et al. Multi-view fuzzy double-weighting possibility clustering algorithm[J]. CAAI transactions on intelligent systems, 2017, 12(6):806-815
[17] LIU Jiefang, CHUNG F L, WANG Shitong. Black hole entropic fuzzy clustering[J]. IEEE transactions on systems, man, and cybernetics:systems, 2018, 48(9):1622-1636.
[18] ZHU Lin, CHUNG F L, WANG Shitong. Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions[J]. IEEE transactions on systems, man, and cybernetics, part b, 2009, 39(3):578-591.
[19] 张远鹏, 邓赵红, 钟富礼, 等. 基于代表点评分策略的快速自适应聚类算法[J]. 计算机研究与发展, 2018, 55(1):163-178 ZHANG Yuanpeng, DENG Zhaohong, ZHONG Fuli, et al. Fast self-adaptive clustering algorithm based on exemplar score strategy[J]. Journal of computer research and development, 2018, 55(1):163-178
[20] DENG Zhaohong, JIANG Yizhang, CHUNG F L, et al. Transfer prototype-based fuzzy clustering[J]. IEEE transactions on fuzzy systems, 2016, 24(5):1210-1232.
[21] ASUNCION A, NEWMAN D J. UCI machine learning repository[EB/OL]. (2009-10-16)[2019-05-10] http://archive.ics.uci.edu/ml.
[22] ZHOU Zhihua, LI Ming. Tri-training:exploiting unlabeled data using three classifiers[J]. IEEE transactions on knowledge and data engineering, 2005, 17(11):1529-1541.
[23] WANG Shuo, YAO Xin. Relationships between diversity of classification ensembles and single-class performance measures[J]. IEEE transactions on knowledge and data engineering, 2013, 25(1):206-219.
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