[1]刘威,刘尚,白润才,等.动态数据约简的神经网络分类器训练方法研究[J].智能系统学报,2017,12(2):2258-2265.[doi:10.11992/tis.201605031]
LIU Wei,LIU Shang,BAI Runcai,et al.Reducing training times in neural network classifiers by using dynamic data reduction[J].CAAI Transactions on Intelligent Systems,2017,12(2):2258-2265.[doi:10.11992/tis.201605031]
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LIU Wei,LIU Shang,BAI Runcai,et al.Reducing training times in neural network classifiers by using dynamic data reduction[J].CAAI Transactions on Intelligent Systems,2017,12(2):2258-2265.[doi:10.11992/tis.201605031]
动态数据约简的神经网络分类器训练方法研究
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
12
期数:
2017年第2期
页码:
2258-2265
栏目:
学术论文—机器学习
出版日期:
2017-05-05
- Title:
- Reducing training times in neural network classifiers by using dynamic data reduction
- Keywords:
- neural network; data reduction; classification boundary; sample weight; boundary sample; kernel sample
- 分类号:
- TP301.6
- 摘要:
- 针对神经网络分类器训练时间长、泛化能力差的问题，提出了一种基于动态数据约简的神经网络分类器训练方法（DDR）。该训练方法在训练过程中赋给每个训练样本一个权重值作为样本的重要性度量，依据每次网络迭代训练样本的分类错误率动态更新每个训练样本的权重值，之后依据样本的权重值来约简训练样本，从而增加易错分类的边界样本比重，减少冗余核样本的作用。数值实验表明，基于权重的动态数据约简神经网络训练方法不仅大幅缩短了网络的训练时间，而且还能够显著提升网络的分类泛化能力。
- Abstract:
- In this paper, we present a neural network classifier training method based on dynamic data reduction (DDR) to address long training times and the poor generalization ability of neural network classifiers. In our approach, we assigned each sample a weight value, which was then dynamically updated based on the classification error rate at each iteration of the training sample. Subsequently, the training sample was reduced based on the weight of the sample so as to increase the proportion of boundary samples in error-prone classification environments and to reduce the role of redundant kernel samples. Our numerical experiments show that our neural network training method not only substantially shortens the training time of the given networks, but also significantly enhances the classification and generalization abilities of the network.
- 参考文献/References:
-
[1] 毛勇. 基于支持向量机的特征选择方法的研究与应用[D]. 杭州: 浙江大学, 2006. MAO Yong. A study on feature selection algorithms based on support vector machine and its application[D]. Hangzhou: Zhejiang University, 2006.
[2] 覃政仁, 吴渝, 王国胤. 一种基于Rough Set的海量数据分割算法[J]. 模式识别与人工智能, 2006, 19(2): 249-256.QIN Zhengren, WU Yu, WANG Guoyin. A partition algorithm for huge data sets based on rough set[J]. Pattern recognition and artificial intelligence, 2006, 19(2): 249-256.
[3] ABDI H, WILLIAMS L J. Principal component analysis[J]. Wiley interdisciplinary reviews: computational statistics, 2010, 2(4): 433-459.
[4] RIFAI S, VINCENT P, MULLER X, et al. Contractive auto-encoders: explicit invariance during feature extraction[C]//Proceedings of the 28th International Conference on Machine Learning. Bellevue, WA, USA: ICML, 2011.
[5] 周玉, 朱安福, 周林, 等. 一种神经网络分类器样本数据选择方法[J]. 华中科技大学学报:自然科学版, 2012, 40(6): 39-43. ZHOU Yu, ZHU Anfu, ZHOU Lin, et al. Sample data selection method for neural network classifier[J]. Journal of Huazhong university of science and technology: natural science edition, 2012, 40(6): 39-43.
[6] 郝红卫, 蒋蓉蓉. 基于最近邻规则的神经网络训练样本选择方法[J]. 自动化学报, 2007, 33(12): 1247-1251. HAO Hongwei, JIANG Rongrong. Training sample selection method for neural networks based on nearest neighbor rule[J]. Acta automatica sinica, 2007, 33(12): 1247-1251.
[7] HARA K, NAKAYAMA K. A training method with small computation for classification[C]//Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. Como, Italy: IEEE, 2000: 543-548.
[8] 邓乃扬, 田英杰. 数据挖掘中的新方法——支持向量机[M]. 北京: 科学出版社, 2004.
[9] 刘刚, 张洪刚, 郭军. 不同训练样本对识别系统的影响[J]. 计算机学报, 2005, 28(11): 1923-1928. LIU Gang, ZHANG Honggang, GUO Jun. The influence of different training samples to recognition system[J]. Chinese journal of computers, 2005, 28(11): 1923-1928.
[10] SCHAPIRE R E, SINGER Y. Improved boosting algorithms using confidence-rated predictions[J]. Machine learning, 1999, 37(3): 297-336.
[11] 韦岗, 贺前华. 神经网络模型学习及应用[M]. 北京: 电子工业出版社, 1994.
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备注/Memo
收稿日期:2016-5-28;改回日期:。
基金项目:国家自然科学基金项目（51304114，71371091）．
作者简介:刘威,男,1977年生,副教授,博士,中国计算机学会会员,主要研究方向为人工智能与模式识别、机器学习、露天采矿系统工程;刘尚,男,1988年生,硕士研究生,主要研究方向为人工智能与模式识别、机器学习、计算机视觉;白润才,男,1962年生,教授,博士生导师,主要研究方向为数字矿山、露天开采系统工程。
通讯作者:刘尚. E-mail:whiteinblue@126.com.
更新日期/Last Update:
1900-01-01