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A Simple yet Effective Framework for active learning to Rank

Machine Intelligence Research 2024年第1期21卷 169-183页

作者： Qingzhong Wang Haifang Li Haoyi Xiong Wen Wang Jiang Bian Yu Lu Shuaiqiang Wang Zhicong Cheng Dejing Dou Dawei Yin Baidu Incorporated Beijing 100085China

While China has become the largest online market in the world with approximately 1 billion internet users,Baidu runs the world's largest Chinese search engine serving more than hundreds of millions of daily active users and responding to billions of queries per day.To handle the diverse query requests from users at the web-scale,Baidu has made tremendous efforts in understanding users'queries,retrieving relevant content from a pool of trillions of webpages,and ranking the most relevant webpages on the top of the res-ults.Among the components used in Baidu search,learning to rank(LTR)plays a critical role and we need to timely label an extremely large number of queries together with relevant webpages to train and update the online LTR models.To reduce the costs and time con-sumption of query/webpage labelling,we study the problem of active learning to rank(active LTR)that selects unlabeled queries for an-notation and training in this work.Specifically,we first investigate the criterion-Ranking entropy(RE)characterizing the entropy of relevant webpages under a query produced by a sequence of online LTR models updated by different checkpoints,using a query-by-com-mittee(QBC)method.Then,we explore a new criterion namely prediction variances(PV)that measures the variance of prediction res-ults for all relevant webpages under a query.Our empirical studies find that RE may favor low-frequency queries from the pool for la-belling while PV prioritizes high-frequency queries more.Finally,we combine these two complementary criteria as the sample selection strategies for active learning.Extensive experiments with comparisons to baseline algorithms show that the proposed approach could train LTR models to achieve higher discounted cumulative gain(i.e.,the relative improvement DCG4=1.38%)with the same budgeted labellingefforts.

关键词： Search information retrieval learning to rank active learning query by committee

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active learning accelerated Monte-Carlo simulation based on the modified K-nearest neighbors algorithm and its application to reliability estimations

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Defence Technology（防务技术） 2023年第10期28卷 306-313页

作者： Zhifeng Xu Jiyin Cao Gang Zhang Xuyong Chen Yushun Wu School of Civil Engineering and Architecture Wuhan Institute of TechnologyWuhan430200PR China School of Mechanical and Electrical Engineering Wuhan Institute of TechnologyWuhan430200PR China Failure Mechanics&Engineering Disaster Prevention and Mitigation Key Laboratory of Sichuan ProvinceSichuan UniversityChengdu610065PR China

This paper proposes an active learning accelerated Monte-Carlo simulation method based on the modified K-nearest neighbors algorithm.The core idea of the proposed method is to judge whether or not the output of a random input point can be postulated through a classifier implemented through the modified K-nearest neighbors algorithm.Compared to other active learning methods resorting to experimental designs,the proposed method is characterized by employing Monte-Carlo simulation for sampling inputs and saving a large portion of the actual evaluations of outputs through an accurate classification,which is applicable for most structural reliability estimation problems.Moreover,the validity,efficiency,and accuracy of the proposed method are demonstrated numerically.In addition,the optimal value of K that maximizes the computational efficiency is studied.Finally,the proposed method is applied to the reliability estimation of the carbon fiber reinforced silicon carbide composite specimens subjected to random displacements,which further validates its practicability.

关键词： active learning Monte-carlo simulation K-nearest neighbors Reliability estimation Classification

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active learning Strategies for Textual Dataset-Automatic Labelling

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Computers, Materials & Continua 2023年第8期76卷 1409-1422页

作者： Sher Muhammad Daudpota Saif Hassan Yazeed Alkhurayyif Abdullah Saleh Alqahtani Muhammad Haris Aziz Department of Computer Science Sukkur IBA UniversitySukkur65200Pakistan Al Quwayiyah College of Sciences and Humanities Shaqra UniversityShaqra15526Saudi Arabia Self-Development Skills Department Common First Year DeanshipKing Saud UniversityRiyadh12373Saudi Arabia STC’s Artificial Intelligence Chair Department of Information SystemsCollege of Computer and Information SciencesKing Saud UniversityRiyadh11451Saudi Arabia College of Engineering&Technology University of SargodhaSargodha40100Pakistan

The Internet revolution has resulted in abundant data from various sources,including social media,traditional media,etcetera.Although the availability of data is no longer an issue,data labelling for exploiting it in supervised machine learning is still an expensive process and involves tedious human efforts.The overall purpose of this study is to propose a strategy to automatically label the unlabeled textual data with the support of active learning in combination with deep learning.More specifically,this study assesses the performance of different active learning strategies in automatic labelling of the textual dataset at sentence and document levels.To achieve this objective,different experiments have been performed on the publicly available dataset.In first set of experiments,we randomly choose a subset of instances from training dataset and train a deep neural network to assess performance on test set.In the second set of experiments,we replace the random selection with different active learning strategies to choose a subset of the training dataset to train the same model and reassess its performance on test set.The experimental results suggest that different active learning strategies yield performance improvement of 7% on document level datasets and 3%on sentence level datasets for auto labelling.

关键词： active learning automatic labelling textual datasets

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Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification

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Frontiers of Information Technology & Electronic Engineering 2022年第12期23卷 1814-1827页

作者： Weijun WANG Yun WANG Jun WANG Xinyun FANG Yuchen HE Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang UniversityHangzhou 310018China Mechanical and Electrical Engineering Department Zhejiang Tongji Vocational College of Science and TechnologyHangzhou 311231China Suzhou Institute of Metrology Suzhou 215004China

As an indispensable part of process monitoring, the performance of fault classification relies heavily on the sufficiency of process knowledge. However, data labels are always difficult to acquire because of the limited sampling condition or expensive laboratory analysis, which may lead to deterioration of classification performance.To handle this dilemma, a new semi-supervised fault classification strategy is performed in which enhanced active learning is employed to evaluate the value of each unlabeled sample with respect to a specific labeled dataset.Unlabeled samples with large values will serve as supplementary information for the training dataset. In addition,we introduce several reasonable indexes and criteria, and thus human labeling interference is greatly reduced. Finally,the fault classification effectiveness of the proposed method is evaluated using a numerical example and the Tennessee Eastman process.

关键词： Semi-supervised active learning Ensemble learning Mixture discriminant analysis Fault classification

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active label distribution learning via kernel maximum mean discrepancy

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Frontiers of Computer Science 2023年第4期17卷 69-81页

作者： Xinyue DONG Tingjin LUO Ruidong FAN Wenzhang ZHUGE Chenping HOU College of Science National University of Defense TechnologyChangsha 410073China

Label distribution learning(LDL)is a new learning paradigm to deal with label ambiguity and many researches have achieved the prominent performances.Compared with traditional supervised learning scenarios,the annotation with label distribution is more expensive.Direct use of existing active learning(AL)approaches,which aim to reduce the annotation cost in traditional learning,may lead to the degradation of their performance.To deal with the problem of high annotation cost in LDL,we propose the active label distribution learning via kernel maximum mean discrepancy(ALDL-kMMD)method to tackle this crucial but rarely studied problem.ALDL-kMMD captures the structural information of both data and label,extracts the most representative instances from the unlabeled ones by incorporating the nonlinear model and marginal probability distribution matching.Besides,it is also able to markedly decrease the amount of queried unlabeled instances.Meanwhile,an effective solution is proposed for the original optimization problem of ALDL-kMMD by constructing auxiliary variables.The effectiveness of our method is validated with experiments on the real-world datasets.

关键词： label distribution learning active learning maximum mean discrepancy auxiliary variable

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Phase prediction for high-entropy alloys using generative adversarial network and active learning based on small datasets

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Science China(Technological Sciences) 2023年第12期66卷 3615-3627页

作者： CHEN Cun ZHOU HengRu LONG WeiMin WANG Gang REN JingLi Henan Academy of Big Data School of Mathematics and StatisticsZhengzhou UniversityZhengzhou 450001China State Key Laboratory of Advanced Brazing Filler Metals and Technology Zhengzhou Research Institute of Mechanical EngineeringZhengzhou 450001China Institute of Materials Shanghai UniversityShanghai 200444China

In this paper,a new machine learning(ML)model combining conditional generative adversarial networks(CGANs)and active learning(AL)is proposed to predict the body-centered cubic(BCC)phase,face-centered cubic(FCC)phase,and BCC+FCC phase of high-entropy alloys(HEAs).Considering the lack of data,CGANs are introduced for data augmentation,and AL can achieve high prediction accuracy under a small sample size owing to its special sample selection strategy.Therefore,we propose an ML framework combining CGAN and AL to predict the phase of HEAs.The arithmetic optimization algorithm(AOA)is introduced to improve the artificial neural network(ANN).AOA can overcome the problem of falling into the locally optimal solution for the ANN and reduce the number of training iterations.The AOA-optimized ANN model trained by the AL sample selection strategy achieved high prediction accuracy on the test set.To improve the performance and interpretability of the model,domain knowledge is incorporated into the feature selection.Additionally,considering that the proposed method can alleviate the problem caused by the shortage of experimental data,it can be applied to predictions based on small datasets in other fields.

关键词： high-entropy alloys phase prediction machine learning conditional generative adversarial networks active learning

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Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm

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International Journal of Ophthalmology(English edition) 2023年第7期16卷 995-1004页

作者： Shao-Jun Zhu Hao-Dong Zhan Mao-Nian Wu Bo Zheng Bang-Quan Liu Shao-Chong Zhang Wei-Hua Yang Huzhou University School of Information EngineeringHuzhou 313000Zhejiang ProvinceChina Zhejiang Province Key Laboratory of Smart Management&Application of Modern Agricultural Resources Huzhou UniversityHuzhou 313000Zhejiang ProvinceChina College of Digital Technology and Engineering Ningbo University of Finance&EconomicsNingbo 315175Zhejiang ProvinceChina Shenzhen Eye Institute Shenzhen Eye HospitalJinan UniversityShenzhen 518048Guangdong ProvinceChina

AIM:To conduct a classification study of high myopic maculopathy(HMM)using limited datasets,including tessellated fundus,diffuse chorioretinal atrophy,patchy chorioretinal atrophy,and macular atrophy,and minimize annotation costs,and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification.METHODS:The optimized ALFA-Mix algorithm(ALFAMix+)was compared with five algorithms,including ALFA-Mix.Four models,including Res Net18,were established.Each algorithm was combined with four models for experiments on the HMM dataset.Each experiment consisted of 20 active learning rounds,with 100 images selected per round.The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+outperformed other algorithms.Finally,this study employed six models,including Efficient Former,to classify HMM.The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+algorithm to achieve satisfactor y classification results with a small dataset.RESULTS:ALFA-Mix+outperforms other algorithms with an average superiority of 16.6,14.75,16.8,and 16.7 rounds in terms of accuracy,sensitivity,specificity,and Kappa value,respectively.This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images.The Efficient Former achieved the best results with an accuracy,sensitivity,specificity,and Kappa value of 0.8821,0.8334,0.9693,and 0.8339,respectively.Therefore,by combining ALFA-Mix+with Efficient Former,this study achieved results with an accuracy,sensitivity,specificity,and Kappa value of 0.8964,0.8643,0.9721,and 0.8537,respectively.CONCLUSION:The ALFA-Mix+algorithm reduces the required samples without compromising accuracy.Compared to other algorithms,ALFA-Mix+outperforms in more rounds of experiments.It effectively selects valuable samples compared to other algorithms.In HMM classification,combining ALFA-Mix+with Efficient Former enhances model performance,further demonstrating the effe

关键词： high myopic maculopathy deep learning active learning image classification ALFA-Mix algorithm

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Deep active learning for Solvability Prediction in Power Systems

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Journal of Modern Power Systems and Clean Energy 2022年第6期10卷 1773-1777页

作者： Yichen Zhang Jianzhe Liu Feng Qiu Tianqi Hong Rui Yao Argonne National Laboratory LemontIL 60439USA IEEE

Traditional methods for solvability region analysis can only have inner approximations with inconclusive conservatism and handle limited types of power flow models.In this letter,we propose a deep active learning framework for solvability prediction in power systems.Compared with passive learning where the training is performed after all instances are labeled,active learning selects most informative instances to be labeled and therefore significantly reduces the size of the labeled dataset for training.In the active learning framework,the acquisition functions,which correspond to different sampling strategies,are defined in terms of the on-the-fly posterior probability from the classifier.First,the IEEE 39-bus system is employed to validate the proposed framework,where a two-dimensional case is illustrated to visualize the effectiveness of the sampling method followed by the high-dimensional numerical experiments.Then,the Northeast Power Coordinating Council(NPCC)140-bus system is used to validate the performance on large-scale power systems.

关键词： active learning deep learning power flow solvability power flow equation

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active Machine learning for Chemical Engineers:A Bright Future Lies Ahead!

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Engineering 2023年第8期27卷 23-30页

作者： Yannick Ureel Maarten R.Dobbelaere Yi Ouyang Kevin De Ras Maarten K.Sabbe Guy B.Marin Kevin M.Van Geem Laboratory for Chemical Technology Department of MaterialsTextiles and Chemical EngineeringGhent UniversityGhent 9052Belgium

By combining machine learning with the design of experiments,thereby achieving so-called active machine learning,more efficient and cheaper research can be conducted.Machine learning algorithms are more flexible and are better than traditional design of experiment algorithms at investigating processes spanning all length scales of chemical engineering.While active machine learning algorithms are maturing,their applications are falling behind.In this article,three types of challenges presented by active machine learning—namely,convincing the experimental researcher,the flexibility of data creation,and the robustness of active machine learning algorithms—are identified,and ways to overcome them are discussed.A bright future lies ahead for active machine learning in chemical engineering,thanks to increasing automation and more efficient algorithms that can drive novel discoveries.

关键词： active machine learning active learning Bayesian optimization Chemical engineering Design of experiments

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active learning based on belief functions

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Science China(Information Sciences) 2020年第11期63卷 69-83页

作者： Shixing ZHANG Deqiang HAN Yi YANG Institute of Integrated Automation Xi'an Jiaotong University School of Aerospace Xi'an Jiaotong University

active learning involves selecting a few critical unlabeled samples for manual and credible labeling to improve the performance of the current classifier. The critical step of active learning is the sample selection strategy. Uncertainty sampling is a well-known sample selection strategy, which involves selecting the samples for which the current classifier is uncertain. For the generalized linear model, these samples are usually distributed around the current classification hyperplane. However, uncertain samples include samples near the current classification hyperplane, and samples far from the current classification hyperplane and the labeled samples. Traditional uncertainty sampling fails to describe the latter, and traditional methods are easily affected by outliers. In this paper, belief functions are used to describe the uncertainty that exists in various samples. Furthermore, we propose a sample selection strategy based on belief functions.Experimental results based on benchmark datasets show that the proposed approach outperforms several classical methods. Through this approach, higher classification accuracy can be achieved using the same number of new labeled samples.

关键词： active learning uncertainty sampling belief functions generalized linear model

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