基于分類誤差一致性準(zhǔn)則的自適應(yīng)知識遷移

梁爽; 杭文龍; 馮偉; 劉學(xué)軍

doi:10.11999/JEIT181054

基于分類誤差一致性準(zhǔn)則的自適應(yīng)知識遷移

doi: 10.11999/JEIT181054

1.
南京郵電大學(xué)地理與生物信息學(xué)院 ??南京 ??210023
2.
南京工業(yè)大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院南京 211816

基金項(xiàng)目: 國家自然科學(xué)基金(61802177)，江蘇省高校自然科學(xué)研究面上項(xiàng)目(18KJB520020)，南京郵電大學(xué)引進(jìn)人才科研啟動基金(NY219034)，江蘇省重點(diǎn)研發(fā)計(jì)劃(BE2015697)

詳細(xì)信息

作者簡介:
梁爽：女，1987年生，講師，研究方向?yàn)闄C(jī)器學(xué)習(xí)、信號處理

杭文龍：男，1988年生，講師，研究方向?yàn)闄C(jī)器學(xué)習(xí)、模式識別

馮偉：男，1995年生，碩士生，研究方向機(jī)器學(xué)習(xí)、模式識別

劉學(xué)軍：男，1970年生，教授，碩士生導(dǎo)師，研究方向?yàn)閿?shù)據(jù)挖掘、大數(shù)據(jù)分布式處理

通訊作者:
杭文龍　wlhang@njtech.edu.cn

中圖分類號: TP181
計(jì)量
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出版歷程
- 收稿日期: 2018-11-20
- 修回日期: 2019-04-30
- 網(wǎng)絡(luò)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: 目前大多數(shù)遷移學(xué)習(xí)方法在利用源域數(shù)據(jù)輔助目標(biāo)域數(shù)據(jù)建模時，通常假設(shè)源域中的數(shù)據(jù)均與目標(biāo)域數(shù)據(jù)相關(guān)。然而在實(shí)際應(yīng)用中，源域中的數(shù)據(jù)并非都與目標(biāo)域數(shù)據(jù)的相關(guān)程度一致，若基于上述假設(shè)往往會導(dǎo)致負(fù)遷移效應(yīng)。為此，該文首先提出分類誤差一致性準(zhǔn)則(CCR)，對源域與目標(biāo)域分類誤差的概率分布積分平方誤差進(jìn)行最小化度量。此外，該文提出一種基于CCR的自適應(yīng)知識遷移學(xué)習(xí)方法(CATL)，該方法可以快速地從源域中自動確定出與目標(biāo)域相關(guān)的數(shù)據(jù)及其權(quán)重，以輔助目標(biāo)域模型的構(gòu)建，使其能在提高知識遷移效率的同時緩解負(fù)遷移學(xué)習(xí)效應(yīng)。在真實(shí)圖像以及文本數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果驗(yàn)證了CATL方法的優(yōu)勢。
- 遷移學(xué)習(xí) /
- 負(fù)遷移 /
- 概率分布 /
- 分類誤差一致性規(guī)則
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

圖 1 6種對比算法在文本數(shù)據(jù)集上的分類精度

下載: 全尺寸圖片幻燈片

表 1 圖像數(shù)據(jù)集USPS及MNIST中源域數(shù)據(jù)與目標(biāo)域數(shù)據(jù)的詳細(xì)設(shè)置

任務(wù)	源域數(shù)據(jù)		目標(biāo)域數(shù)據(jù)
任務(wù)	正類	負(fù)類	正類	負(fù)類
1	USPS7	USPS9	MNIST7	MNIST9
2	USPS4	USPS9	MNIST4	MNIST9
3	USPS0	USPS6	MNIST0	MNIST6

下載: 導(dǎo)出CSV

表 2 文本數(shù)據(jù)集20-Newsgroups中源域數(shù)據(jù)與目標(biāo)域數(shù)據(jù)的詳細(xì)設(shè)置

任務(wù)	源域數(shù)據(jù)		目標(biāo)域數(shù)據(jù)
任務(wù)	正類	負(fù)類	正類	負(fù)類
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

下載: 導(dǎo)出CSV

表 3 各種算法在圖像任務(wù)上的分類精度

任務(wù)	已標(biāo)注樣本	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

下載: 導(dǎo)出CSV

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