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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 *** 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 *** 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 *** 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 ***,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)***,we explore a new criterion namely prediction variances(PV)that measures the variance of prediction res-ults for all relevant webpages under a *** empirical studies find that RE may favor low-frequency queries from the pool for la-belling while PV prioritizes high-frequency queries ***,we combine these two complementary criteria as the sample selection strategies for active *** 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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Model Change active learning in Graph-Based Semi-supervised learning

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Communications on Applied Mathematics and Computation 2024年第2期6卷 1270-1298页

作者： Kevin S.Miller Andrea L.Bertozzi Department of Mathematics University of CaliforniaLos Angeles520 Portola PlazaLos AngelesCA 90095USA

active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier.A challenge is to identify which points to label to best improve performance while limiting the number of new labels."Model Change"active learning quantifies the resulting change incurred in the classifier by introducing the additional label(s).We pair this idea with graph-based semi-supervised learning(SSL)methods,that use the spectrum of the graph Laplacian matrix,which can be truncated to avoid prohibitively large computational and storage *** consider a family of convex loss functions for which the acquisition function can be efficiently approximated using the Laplace approximation of the posterior *** show a variety of multiclass examples that illustrate improved performance over prior state-of-art.

关键词： active learning Graph-based methods Semi-supervised learning(SSL) Graph Laplacian

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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 *** 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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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 *** 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 *** 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 ***,the validity,efficiency,and accuracy of the proposed method are demonstrated *** addition,the optimal value of K that maximizes the computational efficiency is ***,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,*** 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 *** 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 *** specifically,this study assesses the performance of different active learning strategies in automatic labelling of the textual dataset at sentence and document *** achieve this objective,different experiments have been performed on the publicly available *** 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 *** 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 *** 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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active learning based on maximizing information gain for content-based image retrieval

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Journal of Southeast University(English Edition) 2004年第4期20卷 431-435页

作者：徐杰施鹏飞上海交通大学电子信息与电气工程学院上海200030

This paper describes a new method for active learning in content-based image retrieval. The proposed method firstly uses support vector machine (SVM) classifiers to learn an initial query concept. Then the proposed active learning scheme employs similarity measure to check the current version space and selects images with maximum expected information gain to solicit user's label. Finally, the learned query is refined based on the user's further feedback. With the combination of SVM classifier and similarity measure, the proposed method can alleviate model bias existing in each of them. Our experiments on several query concepts show that the proposed method can learn the user's query concept quickly and effectively only with several iterations.

关键词： active learning content-based image retrieval relevance feedback support vector machines similarity measure

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A genetic algorithm based entity resolution approach with active learning

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Frontiers of Computer Science 2017年第1期11卷 147-159页

作者： Chenchen SUN Derong SHEN Yue KOU Tiezheng NIE Ge YU Sohool of Information Science and Engineering Northeastern University Shenyang 110819 China

Entity resolution is a key aspect in data quality and data integration, identifying which records correspond to the same real world entity in data sources. Many existing ap- proaches require manually designed match rules to solve the problem, which always needs domain knowledge and is time consuming. We propose a novel genetic algorithm based en- tity resolution approach via active learning. It is able to learn effective match rules by logically combining several different attributes＇ comparisons with proper thresholds. We use ac- tive learning to reduce manually labeled data and speed up the learning process. The extensive evaluation shows that the proposed approach outperforms the sate-of-the-art entity res- olution approaches in accuracy.

关键词： entity resolution genetic algorithm active learning data quality data integration

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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 *** 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 *** 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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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 ***,we propose an ML framework combining CGAN and AL to predict the phase of *** 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 *** AOA-optimized ANN model trained by the AL sample selection strategy achieved high prediction accuracy on the test *** improve the performance and interpretability of the model,domain knowledge is incorporated into the feature ***,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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Important sampling based active learning for imbalance classification

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Science China(Information Sciences) 2020年第8期63卷 196-209页

作者： Xinyue WANG Bo LIU Siyu CAO Liping JING Jian YU School of Computer and Information Technology Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University College of Information Science and Technology Hebei Agricultural University

Imbalance in data distribution hinders the learning performance of classifiers. To solve this problem, a popular type of methods is based on sampling(including oversampling for minority class and undersampling for majority class) so that the imbalanced data becomes relatively balanced data. However,they usually focus on one sampling technique, oversampling or undersampling. Such strategy makes the existing methods suffer from the large imbalance ratio(the majority instances size over the minority instances size). In this paper, an active learning framework is proposed to deal with imbalanced data by alternative performing important sampling(ALIS), which consists of selecting important majority-class instances and generating informative minority-class instances. In ALIS, two important sampling strategies affect each other so that the selected majority-class instances provide much clearer information in the next oversampling process, meanwhile the generated minority-class instances provide much more sufficient information for the next undersampling procedure. Extensive experiments have been conducted on real world datasets with a large range of imbalance ratio to verify ALIS. The experimental results demonstrate the superiority of ALIS in terms of several well-known evaluation metrics by comparing with the state-of-the-art methods.

关键词： imbalance classification important sampling active learning oversampling undersampling

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