Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundan...Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.展开更多
Based on linguistic evaluations, a linguistic threeway decision method is proposed. First, the alternatives are rated in linguistic forms and divided into acceptance, rejection and uncertainty regions. Secondly, the l...Based on linguistic evaluations, a linguistic threeway decision method is proposed. First, the alternatives are rated in linguistic forms and divided into acceptance, rejection and uncertainty regions. Secondly, the linguistic three-way group decision steps are provided. Specifically, the experts determine the lower bound and upper bound of the uncertainty region, respectively. When the evaluation is superior to the upper bound, the corresponding alternative is put into the acceptance region directly. Similarly, when the evaluation is inferior to the lower bound, the corresponding alternative is put into the rejection region directly, and the remaining alternatives are put into the uncertain region. Moreover, the objects in the uncertainty region are especially discussed. The linguistic terms are transformed into fuzzy numbers and then aggregated. Finally, a recommendation example is provided to illustrate the practicality and validity of the proposed method.展开更多
Real-time resource allocation is crucial for phased array radar to undertake multi-task with limited resources,such as the situation of multi-target tracking,in which targets need to be prioritized so that resources c...Real-time resource allocation is crucial for phased array radar to undertake multi-task with limited resources,such as the situation of multi-target tracking,in which targets need to be prioritized so that resources can be allocated accordingly and effectively.A three-way decision-based model is proposed for adaptive scheduling of phased radar dwell time.Using the model,the threat posed by a target is measured by an evaluation function,and therefore,a target is assigned to one of the three possible decision regions,i.e.,positive region,negative region,and boundary region.A different region has a various priority in terms of resource demand,and as such,a different radar resource allocation decision is applied to each region to satisfy different tracking accuracies of multi-target.In addition,the dwell time scheduling model can be further optimized by implementing a strategy for determining a proper threshold of three-way decision making to optimize the thresholds adaptively in real-time.The advantages and the performance of the proposed model have been verified by experimental simulations with comparison to the traditional twoway decision model and the three-way decision model without threshold optimization.The experiential results demonstrate that the performance of the proposed model has a certain advantage in detecting high threat targets.展开更多
Three-way decision(T-WD)theory is about thinking,problem solving,and computing in threes.Behavioral decision making(BDM)focuses on effective,cognitive,and social processes employed by humans for choosing the optimal o...Three-way decision(T-WD)theory is about thinking,problem solving,and computing in threes.Behavioral decision making(BDM)focuses on effective,cognitive,and social processes employed by humans for choosing the optimal object,of which prospect theory and regret theory are two widely used tools.The hesitant fuzzy set(HFS)captures a series of uncertainties when it is difficult to specify precise fuzzy membership grades.Guided by the principles of three-way decisions as thinking in threes and integrating these three topics together,this paper reviews and examines advances in three-way behavioral decision making(TW-BDM)with hesitant fuzzy information systems(HFIS)from the perspective of the past,present,and future.First,we provide a brief historical account of the three topics and present basic formulations.Second,we summarize the latest development trends and examine a number of basic issues,such as one-sidedness of reference points and subjective randomness for result values,and then report the results of a comparative analysis of existing methods.Finally,we point out key challenges and future research directions.展开更多
Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential te...Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential test strategy （DSTS） for EHM is presented. Considering the situation that equipment health state is not completely observable in reality, a DSTS optimization method based on partially observable semi-Markov decision pro- cess （POSMDP） is proposed. Firstly, an equipment health state degradation model is constructed by Markov process, and the control limit maintenance policy is also introduced. Secondly, POSMDP is formulated in great detail. And then, POSMDP is converted to completely observable belief semi-Markov decision process （BSMDP） through belief state. The optimal equation and the corresponding optimal DSTS, which minimize the long-run ex- pected average cost per unit time, are obtained with BSMDP. The results of application in complex equipment show that the proposed DSTS is feasible and effective.展开更多
The ensemble learning model can effectively detect drift and utilize diversity to improve the performance of adapting to drift.However,local concept drift can occur in different types at different time points,causing ...The ensemble learning model can effectively detect drift and utilize diversity to improve the performance of adapting to drift.However,local concept drift can occur in different types at different time points,causing basic learners are difficult to distinguish the drift of local boundaries,and the drift range is difficult to determine.Thus,the ensemble learning model to adapt local concept drifts is still challenging problem.Moreover,there are often differences in decision boundaries after drift adaptation,and employing overall diversity measurement is inappropriate.To address these two issues,this paper proposes a novel ensemble learning model called instance-weighted ensemble learning based on the three-way decision(IWE-TWD).In IWE-TWD,a divide-and-conquer strategy is employed to handle uncertain drift and to select base learners;Density clustering dynamically constructs density regions to lock drift range;Three-way decision is adopted to estimate whether the region distribution changes,and the instance is weighted with the probability of region distribution change;The diversities between base learners are determined with three-way decision also.Experimental results show that IWE-TWD has better performance than the state-of-the-art models in data stream classification on ten synthetic data sets and seven real-world data sets.展开更多
In this paper, we propose three-way granular approximations(3WGAs) based on bisimulations. We discover the relationships between 3WGAs based on underlying relations and 3WGAs based on bisimilarity(the largest bisimula...In this paper, we propose three-way granular approximations(3WGAs) based on bisimulations. We discover the relationships between 3WGAs based on underlying relations and 3WGAs based on bisimilarity(the largest bisimulation induced by an underlying relation).展开更多
近年来,随着智慧医疗的日益普及,在线医疗平台已逐步发展为满足大众基本医疗需求的重要渠道。为患者推荐合适的医生是在线问诊中的一个重要过程,优化推荐能力不仅可以提高患者的满意度,还能够推动在线医疗平台的发展。与传统推荐系统不...近年来,随着智慧医疗的日益普及,在线医疗平台已逐步发展为满足大众基本医疗需求的重要渠道。为患者推荐合适的医生是在线问诊中的一个重要过程,优化推荐能力不仅可以提高患者的满意度,还能够推动在线医疗平台的发展。与传统推荐系统不同,医生推荐领域受到隐私保护限制,无法查看患者曾经的诊疗历史,因此模型训练时仅能利用每位患者最近一次的就诊记录,面临严峻的数据稀疏问题。同样,模型预测时也仅能根据患者当前的疾病描述文本进行推荐,而由于患者对疾病描述方式的差异性,模型对不同患者的推荐能力也存在差异,这会使部分患者的需求无法得到满足,进而影响模型整体的推荐能力。基于此,本文提出了一种基于数据增强的医生推荐方法(sequential three-way decision with data augmentation,STWD-NA),通过引入不匹配的医患交互信息扩充训练数据,并利用序贯三支决策的思想训练模型。具体来说,该方法由两部分组成:一方面引入了不匹配交互信息的方法,以缓解训练冷启动问题;另一方面,提出了一种基于序贯三支决策的训练算法,以动态调整模型训练时的关注度。最后,通过好大夫平台上的真实数据集验证了本文所提STWD-NA方法的有效性。展开更多
剩余寿命(Remaining Useful Lifetime,RUL)预测和预测维修决策是预测与健康管理(Prognostics and Health Management,PHM)研究的两大核心内容。RUL预测技术已取得丰硕的成果,预测维修决策研究仍不够深入。现有预测维修决策研究更多关注...剩余寿命(Remaining Useful Lifetime,RUL)预测和预测维修决策是预测与健康管理(Prognostics and Health Management,PHM)研究的两大核心内容。RUL预测技术已取得丰硕的成果,预测维修决策研究仍不够深入。现有预测维修决策研究更多关注如何根据单次预测结果进行单次的维修决策。然而,预测是一个动态过程,相应的维修决策也是动态的,动态序贯的决策中决策的更新与停止问题是一个鲜有关注却值得研究的问题。针对以上问题,对状态可检测的系统,采用Bayesian更新和EM算法相结合的方法,实现了系统RUL和可靠性预测结果的动态更新;在此基础上制定序贯维修策略,依据自适应预测周期和RUL预测结果给出了决策停止的判断准则,在动态冗余的预测结果中选择出真正可执行的最优维修时刻;通过仿真实验验证了所提策略和方法的有效性和适用性。结果表明所提出的方法可有效降低系统的故障概率和运维成本,提升运维效率。展开更多
Early exiting has shown significant potential in accelerating the inference of pre-trained language models(PLMs)by allowing easy samples to exit from shallow layers.However,existing early exiting methods primarily rel...Early exiting has shown significant potential in accelerating the inference of pre-trained language models(PLMs)by allowing easy samples to exit from shallow layers.However,existing early exiting methods primarily rely on local information from individual samples to estimate prediction uncertainty for making exiting decisions,overlooking the global information provided by the sample population.This impacts the estimation of prediction uncertainty,compromising the reliability of exiting de-cisions.To remedy this,inspired by principal component analysis(PCA),the authors define a residual score to capture the deviation of features from the principal space of the sample population,providing a global perspective for estimating prediction uncertainty.Building on this,a two-stage exiting strategy is proposed that integrates global information from residual scores with local information from energy scores at both the decision and feature levels.This strategy incorporates three-way decisions to enable more reliable exiting decisions for boundary region samples by delaying judgement.Extensive experiments on the GLUE benchmark validate that the method achieves an average speed-up ratio of 2.17×across all tasks with minimal per-formance degradation.Additionally,it surpasses the state-of-the-art E-LANG by 11%in model acceleration,along with a performance improvement of 0.6 points,demonstrating a better performance-efficiency trade-off.展开更多
文摘Attribute reduction is a research hotspot in rough set theory. Traditional heuristic attribute reduction methods add the most important attribute to the decision attribute set each time, resulting in multiple redundant attribute calculations, high time consumption, and low reduction efficiency. In this paper, based on the idea of sequential three-branch decision classification domain, attributes are treated as objects of three-branch division, and attributes are divided into core attributes, relatively necessary attributes, and unnecessary attributes using attribute importance and thresholds. Core attributes are added to the decision attribute set, unnecessary attributes are rejected from being added, and relatively necessary attributes are repeatedly divided until the reduction result is obtained. Experiments were conducted on 8 groups of UCI datasets, and the results show that, compared to traditional reduction methods, the method proposed in this paper can effectively reduce time consumption while ensuring classification performance.
基金The National Natural Science Foundation of China(No.71171048,71371049)the Scientific Innovation Research of College Graduates in Jiangsu Province(No.KYLX15-0190)the Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1567)
文摘Based on linguistic evaluations, a linguistic threeway decision method is proposed. First, the alternatives are rated in linguistic forms and divided into acceptance, rejection and uncertainty regions. Secondly, the linguistic three-way group decision steps are provided. Specifically, the experts determine the lower bound and upper bound of the uncertainty region, respectively. When the evaluation is superior to the upper bound, the corresponding alternative is put into the acceptance region directly. Similarly, when the evaluation is inferior to the lower bound, the corresponding alternative is put into the rejection region directly, and the remaining alternatives are put into the uncertain region. Moreover, the objects in the uncertainty region are especially discussed. The linguistic terms are transformed into fuzzy numbers and then aggregated. Finally, a recommendation example is provided to illustrate the practicality and validity of the proposed method.
基金the Aeronautical Science Foundation of China(2017ZC53021)the Open Project Fund of CETC Key Laboratory of Data Link Technology(CLDL-20182101).
文摘Real-time resource allocation is crucial for phased array radar to undertake multi-task with limited resources,such as the situation of multi-target tracking,in which targets need to be prioritized so that resources can be allocated accordingly and effectively.A three-way decision-based model is proposed for adaptive scheduling of phased radar dwell time.Using the model,the threat posed by a target is measured by an evaluation function,and therefore,a target is assigned to one of the three possible decision regions,i.e.,positive region,negative region,and boundary region.A different region has a various priority in terms of resource demand,and as such,a different radar resource allocation decision is applied to each region to satisfy different tracking accuracies of multi-target.In addition,the dwell time scheduling model can be further optimized by implementing a strategy for determining a proper threshold of three-way decision making to optimize the thresholds adaptively in real-time.The advantages and the performance of the proposed model have been verified by experimental simulations with comparison to the traditional twoway decision model and the three-way decision model without threshold optimization.The experiential results demonstrate that the performance of the proposed model has a certain advantage in detecting high threat targets.
基金supported in part by the National Natural Science Foundation of China(12271146,12161036,61866011,11961025,61976120)the Natural Science Key Foundation of Jiangsu Education Department(21KJA510004)Discovery Grant from Natural Science and Engineering Research Council of Canada(NSERC)。
文摘Three-way decision(T-WD)theory is about thinking,problem solving,and computing in threes.Behavioral decision making(BDM)focuses on effective,cognitive,and social processes employed by humans for choosing the optimal object,of which prospect theory and regret theory are two widely used tools.The hesitant fuzzy set(HFS)captures a series of uncertainties when it is difficult to specify precise fuzzy membership grades.Guided by the principles of three-way decisions as thinking in threes and integrating these three topics together,this paper reviews and examines advances in three-way behavioral decision making(TW-BDM)with hesitant fuzzy information systems(HFIS)from the perspective of the past,present,and future.First,we provide a brief historical account of the three topics and present basic formulations.Second,we summarize the latest development trends and examine a number of basic issues,such as one-sidedness of reference points and subjective randomness for result values,and then report the results of a comparative analysis of existing methods.Finally,we point out key challenges and future research directions.
基金supported by the National Natural Science Foundation of China (51175502)
文摘Testing is the premise and foundation of realizing equipment health management （EHM）. To address the problem that the static periodic test strategy may cause deficient test or excessive test, a dynamic sequential test strategy （DSTS） for EHM is presented. Considering the situation that equipment health state is not completely observable in reality, a DSTS optimization method based on partially observable semi-Markov decision pro- cess （POSMDP） is proposed. Firstly, an equipment health state degradation model is constructed by Markov process, and the control limit maintenance policy is also introduced. Secondly, POSMDP is formulated in great detail. And then, POSMDP is converted to completely observable belief semi-Markov decision process （BSMDP） through belief state. The optimal equation and the corresponding optimal DSTS, which minimize the long-run ex- pected average cost per unit time, are obtained with BSMDP. The results of application in complex equipment show that the proposed DSTS is feasible and effective.
基金supported by the Postdoctoral Fellowship Program of CPSF(No.GZB20230092)the China Postdoctoral Science Foundation(No.2023M740383)the Natural Science Foundation of Sichuan Province(No.24NSFSC1654).
文摘The ensemble learning model can effectively detect drift and utilize diversity to improve the performance of adapting to drift.However,local concept drift can occur in different types at different time points,causing basic learners are difficult to distinguish the drift of local boundaries,and the drift range is difficult to determine.Thus,the ensemble learning model to adapt local concept drifts is still challenging problem.Moreover,there are often differences in decision boundaries after drift adaptation,and employing overall diversity measurement is inappropriate.To address these two issues,this paper proposes a novel ensemble learning model called instance-weighted ensemble learning based on the three-way decision(IWE-TWD).In IWE-TWD,a divide-and-conquer strategy is employed to handle uncertain drift and to select base learners;Density clustering dynamically constructs density regions to lock drift range;Three-way decision is adopted to estimate whether the region distribution changes,and the instance is weighted with the probability of region distribution change;The diversities between base learners are determined with three-way decision also.Experimental results show that IWE-TWD has better performance than the state-of-the-art models in data stream classification on ten synthetic data sets and seven real-world data sets.
基金Supported by the National Natural Science of China (Grant Nos. 1152616361473181)+2 种基金the Research Fund of School of Economic, Northwest University of Political Science and Law (Grant No. 19XYKY02)the Youth Academic Innovation Team in Northwest University of Political Science and Lawthe Young Academic Backbone of Chang’an in Northwest University of Political Science and Law
文摘In this paper, we propose three-way granular approximations(3WGAs) based on bisimulations. We discover the relationships between 3WGAs based on underlying relations and 3WGAs based on bisimilarity(the largest bisimulation induced by an underlying relation).
文摘近年来,随着智慧医疗的日益普及,在线医疗平台已逐步发展为满足大众基本医疗需求的重要渠道。为患者推荐合适的医生是在线问诊中的一个重要过程,优化推荐能力不仅可以提高患者的满意度,还能够推动在线医疗平台的发展。与传统推荐系统不同,医生推荐领域受到隐私保护限制,无法查看患者曾经的诊疗历史,因此模型训练时仅能利用每位患者最近一次的就诊记录,面临严峻的数据稀疏问题。同样,模型预测时也仅能根据患者当前的疾病描述文本进行推荐,而由于患者对疾病描述方式的差异性,模型对不同患者的推荐能力也存在差异,这会使部分患者的需求无法得到满足,进而影响模型整体的推荐能力。基于此,本文提出了一种基于数据增强的医生推荐方法(sequential three-way decision with data augmentation,STWD-NA),通过引入不匹配的医患交互信息扩充训练数据,并利用序贯三支决策的思想训练模型。具体来说,该方法由两部分组成:一方面引入了不匹配交互信息的方法,以缓解训练冷启动问题;另一方面,提出了一种基于序贯三支决策的训练算法,以动态调整模型训练时的关注度。最后,通过好大夫平台上的真实数据集验证了本文所提STWD-NA方法的有效性。
文摘剩余寿命(Remaining Useful Lifetime,RUL)预测和预测维修决策是预测与健康管理(Prognostics and Health Management,PHM)研究的两大核心内容。RUL预测技术已取得丰硕的成果,预测维修决策研究仍不够深入。现有预测维修决策研究更多关注如何根据单次预测结果进行单次的维修决策。然而,预测是一个动态过程,相应的维修决策也是动态的,动态序贯的决策中决策的更新与停止问题是一个鲜有关注却值得研究的问题。针对以上问题,对状态可检测的系统,采用Bayesian更新和EM算法相结合的方法,实现了系统RUL和可靠性预测结果的动态更新;在此基础上制定序贯维修策略,依据自适应预测周期和RUL预测结果给出了决策停止的判断准则,在动态冗余的预测结果中选择出真正可执行的最优维修时刻;通过仿真实验验证了所提策略和方法的有效性和适用性。结果表明所提出的方法可有效降低系统的故障概率和运维成本,提升运维效率。
基金supported by the National Natural Science Foundation of China(No.62376198)the National Key Research and Development Program of China(No.2022YFB3104700)the Shanghai Baiyulan Pujiang Project(No.08002360429).
文摘Early exiting has shown significant potential in accelerating the inference of pre-trained language models(PLMs)by allowing easy samples to exit from shallow layers.However,existing early exiting methods primarily rely on local information from individual samples to estimate prediction uncertainty for making exiting decisions,overlooking the global information provided by the sample population.This impacts the estimation of prediction uncertainty,compromising the reliability of exiting de-cisions.To remedy this,inspired by principal component analysis(PCA),the authors define a residual score to capture the deviation of features from the principal space of the sample population,providing a global perspective for estimating prediction uncertainty.Building on this,a two-stage exiting strategy is proposed that integrates global information from residual scores with local information from energy scores at both the decision and feature levels.This strategy incorporates three-way decisions to enable more reliable exiting decisions for boundary region samples by delaying judgement.Extensive experiments on the GLUE benchmark validate that the method achieves an average speed-up ratio of 2.17×across all tasks with minimal per-formance degradation.Additionally,it surpasses the state-of-the-art E-LANG by 11%in model acceleration,along with a performance improvement of 0.6 points,demonstrating a better performance-efficiency trade-off.
