基于分类误差一致性准则的自适应知识迁移

梁爽; 杭文龙; 冯伟; 刘学军

doi:10.11999/JEIT181054

基于分类误差一致性准则的自适应知识迁移

doi: 10.11999/JEIT181054

梁爽¹,
杭文龙^2, ,,
冯伟²,
刘学军²

1.
南京邮电大学地理与生物信息学院南京 210023
2.
南京工业大学计算机科学与技术学院南京 211816

基金项目: 国家自然科学基金(61802177)，江苏省高校自然科学研究面上项目(18KJB520020)，南京邮电大学引进人才科研启动基金(NY219034)，江苏省重点研发计划(BE2015697)

详细信息

作者简介:
梁爽：女，1987年生，讲师，研究方向为机器学习、信号处理

杭文龙：男，1988年生，讲师，研究方向为机器学习、模式识别

冯伟：男，1995年生，硕士生，研究方向机器学习、模式识别

刘学军：男，1970年生，教授，硕士生导师，研究方向为数据挖掘、大数据分布式处理

通讯作者:
杭文龙　wlhang@njtech.edu.cn

中图分类号: TP181
计量
- 文章访问数: 2544
- HTML全文浏览量: 1375
- PDF下载量: 58
- 被引次数: 0
出版历程
- 收稿日期: 2018-11-20
- 修回日期: 2019-04-30
- 网络出版日期: 2019-05-16
- 刊出日期: 2019-11-01

Adaptive Knowledge Transfer Based on Classification-error Consensus Regularization

1.
School of Geographic and Biologic Information, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2.
Department of Computer and Technology, Nanjing Tech University, Nanjing 211816, China

Funds: The National Nature Science Foundation of China (61802177), The Natural Science Foundation of the Higher Education Institutions of Jiangsu Province (18KJB520020), NUPTSF (NY219034), Key Research and Development Program of Jiangsu Province (BE2015697)

摘要

摘要: 目前大多数迁移学习方法在利用源域数据辅助目标域数据建模时，通常假设源域中的数据均与目标域数据相关。然而在实际应用中，源域中的数据并非都与目标域数据的相关程度一致，若基于上述假设往往会导致负迁移效应。为此，该文首先提出分类误差一致性准则(CCR)，对源域与目标域分类误差的概率分布积分平方误差进行最小化度量。此外，该文提出一种基于CCR的自适应知识迁移学习方法(CATL)，该方法可以快速地从源域中自动确定出与目标域相关的数据及其权重，以辅助目标域模型的构建，使其能在提高知识迁移效率的同时缓解负迁移学习效应。在真实图像以及文本数据集上的实验结果验证了CATL方法的优势。
- 迁移学习 /
- 负迁移 /
- 概率分布 /
- 分类误差一致性规则
Abstract: Most current transfer learning methods are modeled by utilizing the source data with the assumption that all data in the source domain are equally related to the target domain. In many practical applications, however, this assumption may induce negative learning effect when it becomes invalid. To tackle this issue, by minimizing the integrated squared error of the probability distribution of the source and target domain classification errors, the Classification-error Consensus Regularization (CCR) is proposed. Furthermore, CCR-based Adaptive knowledge Transfer Learning (CATL) method is developed to quickly determine the correlative source data and the corresponding weights. The proposed method can alleviate the negative transfer learning effect while improving the efficiency of knowledge transfer. The experimental results on the real image and text datasets validate the advantages of the CATL method.
- Transfer learning /
- Negative transfer /
- Probability distribution /
- Classification-error Consensus Regularization (CCR)

HTML全文

图 1 6种对比算法在文本数据集上的分类精度

下载: 全尺寸图片幻灯片

表 1 图像数据集USPS及MNIST中源域数据与目标域数据的详细设置

任务	源域数据		目标域数据
任务	正类	负类	正类	负类
1	USPS7	USPS9	MNIST7	MNIST9
2	USPS4	USPS9	MNIST4	MNIST9
3	USPS0	USPS6	MNIST0	MNIST6

下载: 导出CSV

表 2 文本数据集20-Newsgroups中源域数据与目标域数据的详细设置

任务	源域数据		目标域数据
任务	正类	负类	正类	负类
1	comp.graphics	rec.autos	comp.os.ms-windows.misc	rec.motorcycles
2	comp.sys.ibm.pc.hardware	rec.sport.baseball	comp.sys.mac.hardware	rec.sport.hokey
3	sci.crypt	talk.politics.guns	sci.electronics	talk.politics.mideast
4	sci.med	talk.politics.misc	sci.space	talk.religion.misc
5	rec.autos	talk.politics.guns	rec.motorcycles	talk.politics.mideast
6	rec.sport.baseball	talk.politics.misc	rec.sport.hokey	talk.religion.misc

下载: 导出CSV

表 3 各种算法在图像任务上的分类精度

任务	已标注样本	LSSVM	CDSVM	ASVM	TrAdaBoost	STM	PRIF	CATL₂
1	4	0.5287	0.5611	0.5913	0.5799	0.6018	0.6245	0.6359
	6	0.5520	0.5800	0.6094	0.6133	0.6298	0.6384	0.6477
	8	0.5897	0.6112	0.6266	0.6007	0.6319	0.6421	0.6528
	10	0.6030	0.6392	0.6502	0.6213	0.6487	0.6539	0.6672
	12	0.6381	0.6461	0.6383	0.6588	0.6643	0.6753	0.6791
	14	0.6541	0.6587	0.6754	0.6682	0.6901	0.6982	0.7014
2	4	0.5354	0.5743	0.5998	0.5887	0.5983	0.6223	0.6133
	6	0.5897	0.5992	0.6293	0.5903	0.6426	0.6478	0.6520
	8	0.6276	0.6387	0.6492	0.6690	0.6803	0.6893	0.6927
	10	0.6508	0.6641	0.6843	0.6905	0.7067	0.7029	0.7168
	12	0.6892	0.6698	0.6988	0.7123	0.7234	0.7326	0.7387
	14	0.7098	0.7156	0.7207	0.7076	0.7266	0.7391	0.7421
3	4	0.6578	0.6903	0.7026	0.6873	0.7235	0.7472	0.7492
	6	0.7013	0.7445	0.7529	0.7354	0.7541	0.7632	0.7726
	8	0.7452	0.7695	0.7721	0.7455	0.7618	07726	0.7829
	10	0.7762	0.7803	0.7789	0.7836	0.7928	0.7918	0.8193
	12	0.7923	0.7944	0.8034	0.7994	0.8288	0.8172	0.8301
	14	0.8234	0.8213	0.8178	0.8145	0.8397	0.8263	0.8452

下载: 导出CSV

参考文献(25)

DENG Zhaohong, JIANG Yizhang, CHOI K S, et al. Knowledge-leverage-based TSK fuzzy system modeling[J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(8): 1200–1212. doi: 10.1109/TNNLS.2013.2253617

DAI Wenyuan, YANG Qiang, XUE Guirong, et al. Boosting for transfer learning[C]. The 24th International Conference on Machine Learning, Corvalis, USA, 2007: 193–200.

JIANG Yizhang, DENG Zhaohong, CHUNG F L, et al. Recognition of epileptic EEG signals using a novel multiview TSK fuzzy system[J]. IEEE Transactions on Fuzzy Systems, 2017, 25(1): 3–20. doi: 10.1109/TFUZZ.2016.2637405

ZHUANG Fuzhen, LUO Ping, DU Changying, et al. Triplex transfer learning: Exploiting both shared and distinct concepts for text classification[J]. IEEE Transactions on Cybernetics, 2014, 44(7): 1191–1203. doi: 10.1109/TCYB.2013.2281451

PAN S J, NI Xiaochuan, SUN Jiantao, et al. Cross-domain sentiment classification via spectral feature alignment[C]. Proceedings of the 19th International Conference on World Wide Web, Raleigh, USA, 2010: 751–760.

ZANG Shaofei, CHENG Yuhu, WANG Xuesong, et al. Semi-supervised transfer discriminant analysis based on cross-domain mean constraint[J]. Artificial Intelligence Review, 2018, 49(4): 581–595. doi: 10.1007/s10462-016-9533-3

WANG Guanjin, ZHANG Guangquan, CHOI K S, et al. Deep additive least squares support vector machines for classification with model transfer[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019, 49(7): 1527–1540. doi: 10.1109/TSMC.2017.2759090

YANG Jun, YAN Rong, and HAUPTMANN A G. Adapting SVM classifiers to data with shifted distributions[C]. The Seventh IEEE International Conference on Data Mining Workshops, Omaha, USA, 2007: 69–76.

JIANG Yizhang, DENG Zhaohong, CHUNG F L, et al. Realizing two-view TSK fuzzy classification system by using collaborative learning[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47(1): 145–160. doi: 10.1109/TSMC.2016.2577558

CHU Wensheng, DE LA TORRE F, and COHN J F. Selective transfer machine for personalized facial action unit detection[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 3515–3522.

GRETTON A, SMOLA A, HUANG Jiayuan, et al. Covariate Shift by Kernel Mean Matching[M]. QUIÑONERO-CANDELA J, SUGIYAMA M, SCHWAIGHOFER A, et al. Dataset Shift in Machine Learning. Cambridge, USA: MIT Press, 2009: 131–160.

CHENG Yuhu, WANG Xuesong, and CAO Ge. Multi-source tri-training transfer learning[J]. IEICE Transactions on Information and Systems, 2014, E97-D(6): 1668–1672. doi: 10.1587/transinf.e97.d.1668

WANG Yunyun, ZHAI Jie, LI Yun, et al. Transfer learning with partial related " instance-feature” knowledge[J]. Neurocomputing, 2018, 310: 115–124. doi: 10.1016/j.neucom.2018.05.029

CHEN Minmin, XU Zhixiang, WEINBERGER K Q, et al. Marginalized denoising autoencoders for domain adaptation[C]. The 29th International Conference on Machine Learning, Edinburgh, Scotland, 2012: 1627–1634.

ZHOU J T, PAN S J, TSANG I W, et al. Hybrid heterogeneous transfer learning through deep learning[C]. The 28th AAAI Conference on Artificial Intelligence, Québec City, Canada, 2014: 2213–2219.

GLOROT X, BORDES A, and BENGIO Y. Domain adaptation for large-scale sentiment classification: A deep learning approach[C]. The 28th International Conference on Machine Learning, Bellevue, Washington, USA, 2011: 513–520.

LONG Mingsheng, WANG Jianmin, CAO Yue, et al. Deep learning of transferable representation for scalable domain adaptation[J]. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(8): 2027–2040. doi: 10.1109/TKDE.2016.2554549

PARZEN E. On estimation of a probability density function and mode[J]. The Annals of Mathematical Statistics, 1962, 33(3): 1065–1076. doi: 10.1214/aoms/1177704472

DENG Zhaohong, CHUNG F L, and WANG Shitong. FRSDE: Fast reduced set density estimator using minimal enclosing ball approximation[J]. Pattern Recognition, 2008, 41(4): 1363–1372. doi: 10.1016/j.patcog.2007.09.013

TOMMASI T, ORABONA F, and CAPUTO B. Learning categories from few examples with multi model knowledge transfer[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(5): 928–941. doi: 10.1109/TPAMI.2013.197

LACOSTE-JULIEN S, SCHMIDT M, and BACH F. A simpler approach to obtaining an O(1/t) convergence rate for the projected stochastic subgradient method[J]. arXiv:1212.2002, 2012.

LONG Mingsheng, WANG Jianmin, DING Guiguang, et al. Transfer learning with graph co-regularization[J]. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(7): 1805–1818. doi: 10.1109/TKDE.2013.97

SUYKENS J A K and VANDEWALLE J. Least squares support vector machine classifiers[J]. Neural Processing Letters, 1999, 9(3): 293–300. doi: 10.1023/a:1018628609742

BART E and ULLMAN S. Cross-generalization: Learning novel classes from a single example by feature replacement[C]. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, USA, 2005: 672–679.

GU Xiaoqing, CHUNG F L, and WANG Shitong. Bayesian Takagi-Sugeno-Kang fuzzy classifier[J]. IEEE Transactions on Fuzzy Systems, 2017, 25(6): 1655–1671. doi: 10.1109/TFUZZ.2016.2617377

施引文献

资源附件(0)

访问统计