2021 IEEE International Conference on Data Mining (ICDM)最新文献_第9页

Online Learning in Variable Feature Spaces with Mixed Data 混合数据下可变特征空间的在线学习

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00028

Yi He, Jiaxian Dong, Bojian Hou, Yu Wang, Fei Wang

{"title":"Online Learning in Variable Feature Spaces with Mixed Data","authors":"Yi He, Jiaxian Dong, Bojian Hou, Yu Wang, Fei Wang","doi":"10.1109/ICDM51629.2021.00028","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00028","url":null,"abstract":"This paper explores a new online learning problem where the data streams are generated from an over-time varying feature space, in which the random variables are of mixed data types including Boolean, ordinal, and continuous. The crux of this setting lies in how to establish the relationship among features, such that the learner can enjoy 1) reconstructed information of the missed-out old features and 2) a jump-start of learning new features with educated weight initialization. Unfortunately, existing methods mainly assume a linear mapping relationship among features or that the multivariate joint distribution could be modeled as Gaussians, limiting their applicability to the mixed data streams. To fill the gap, we in this paper propose to model the complex joint distribution underlying mixed data with Gaussian copula, where the observed features with arbitrary marginals are mapped onto a latent normal space. The feature correlation is approximated in the latent space through an online EM process. Two base learners trained on the observed and latent features are ensembled to expedite convergence, thereby minimizing prediction risk in an online setting. Theoretical and empirical studies substantiate the effectiveness of our proposed approach. Code is released in https://github.com/xiexvying/OVFM.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123775858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Physics Interpretable Shallow-Deep Neural Networks for Physical System Identification with Unobservability 具有不可观测性的物理系统识别的物理可解释浅-深神经网络

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00096

Jingyi Yuan, Yang Weng

{"title":"Physics Interpretable Shallow-Deep Neural Networks for Physical System Identification with Unobservability","authors":"Jingyi Yuan, Yang Weng","doi":"10.1109/ICDM51629.2021.00096","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00096","url":null,"abstract":"The large amount of data collected in complex physical systems allows machine learning models to solve a variety of prediction problems. However, the directly applied learning approaches, especially deep neural networks (DNN), are difficult to balance between universal approximation to minimize error and the interpretability to reveal underlying physical law. Their performance drops even faster with system unobservability (of measurements) issues due to limited measurements. In this paper, we construct the novel physics interpretable shallow-deep neural networks to integrate exact physical interpretation and universal approximation to address the concerns in previous methods. We show that not only the shallow layer of the structural DNN extracts interpretable physical features but also the designed physical-input convex property of the DNN guarantees the true physical function recovery. While input convexity conditions are strict, the proposed model retains the representation capability to universally approximate for the unobservable system regions. We demonstrate its effectiveness by experiments on physical systems. In particular, we implement the proposed model on the forward kinematics and complex power flow reproduction tasks, with or without observability issues. We show that, besides the physical interpretability, our model provides consistently smaller or similar prediction error for system identification, compared to the state-of-art learning methods.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125108575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

PRGAN: Personalized Recommendation with Conditional Generative Adversarial Networks 基于条件生成对抗网络的个性化推荐

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00084

Jing Wen, Bi-Yi Chen, Chang-Dong Wang, Zhihong Tian

{"title":"PRGAN: Personalized Recommendation with Conditional Generative Adversarial Networks","authors":"Jing Wen, Bi-Yi Chen, Chang-Dong Wang, Zhihong Tian","doi":"10.1109/ICDM51629.2021.00084","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00084","url":null,"abstract":"Most of the existing methods define recommendation as regression or classification for user-item interactions and apply discriminative models. However, recommender systems suffer from interaction data sparsity and data noise problems in reality. Recent Generative Adversarial Network-based recommender systems have the potential to solve the aforementioned problems. The negative sampling methods use the generator to collect effective signals from a large amount of unlabeled data to alleviate the data sparsity problem, while they suffer from sparse rewards in the policy gradient training process. The vector reconstruction methods generate user-related vectors for data augmentation to enhance robustness, but they lead to redundant calculation and only take the user as a condition and ignore information conveyed by items. To alleviate the limitations of these methods, we propose a novel framework termed Personalized Recommendation with Conditional Generative Adversarial Networks (PRGAN) to consider both the user and the item subset as conditions and formulate conditional rating vector generation as a user-item matching problem. The sparsity of conditional rating vectors can be controlled in our method, which simplifies the discriminator’s learning task. Experiments are conducted on four datasets to evaluate the effectiveness of the proposed framework.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125209632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Heterogeneous Graph Neural Network with Distance Encoding 基于距离编码的异构图神经网络

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00135

Houye Ji, Cheng Yang, C. Shi, P. Li

{"title":"Heterogeneous Graph Neural Network with Distance Encoding","authors":"Houye Ji, Cheng Yang, C. Shi, P. Li","doi":"10.1109/ICDM51629.2021.00135","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00135","url":null,"abstract":"Heterogeneous graph neural network (HGNN) has shown superior performance and attracted considerable research interest. However, HGNN inherits the limitation of representational power from GNN via learning individual node embeddings based on their neighbors, largely ignoring the potential correlations between nodes. In fact, the complex correlation between nodes (e.g., distance) is crucial for many graph mining tasks. How to establish correlations between multiple node embeddings and improve the representational power of HGNN is still an open problem. To solve it, we propose a heterogeneous distance encoding (HDE) technique to fundamentally improve the representational power of HGNN. Specifically, we define heterogeneous shortest path distance to describe the relative distance between nodes, and then jointly encode such distances for multiple nodes of interest to establish their correlation. By simply injecting the encoded correlation into the neighbor aggregating process, we propose a novel distance encoding based heterogeneous graph neural network (called DHN), which is able to learn more expressive heterogeneous graph representations for downstream tasks. More importantly, the proposed DHN relies only on the graph structure and ensures the inductive ability of HGNN. Significant improvements over four real-world graphs demonstrate the representational power of HDE.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"07 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127193249","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Fair Decision-making Under Uncertainty 不确定性下的公平决策

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00100

Wenbin Zhang, Jeremy C. Weiss

{"title":"Fair Decision-making Under Uncertainty","authors":"Wenbin Zhang, Jeremy C. Weiss","doi":"10.1109/ICDM51629.2021.00100","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00100","url":null,"abstract":"There has been concern within the artificial intelligence (AI) community and the broader society regarding the potential lack of fairness of AI-based decision-making systems. Surprisingly, there is little work quantifying and guaranteeing fairness in the presence of uncertainty which is prevalent in many socially sensitive applications, ranging from marketing analytics to actuarial analysis and recidivism prediction instruments. To this end, we study a longitudinal censored learning problem subject to fairness constraints, where we require that algorithmic decisions made do not affect certain individuals or social groups negatively in the presence of uncertainty on class label due to censorship. We argue that this formulation has a broader applicability to practical scenarios concerning fairness. We show how the newly devised fairness notions involving censored information and the general framework for fair predictions in the presence of censorship allow us to measure and mitigate discrimination under uncertainty that bridges the gap with real-world applications. Empirical evaluations on real-world discriminated datasets with censorship demonstrate the practicality of our approach.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127348920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

DhakaNet: Unstructured Vehicle Detection using Limited Computational Resources 基于有限计算资源的非结构化车辆检测

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00172

Tarik Reza Toha, Masfiqur Rahaman, Saiful Islam Salim, M. Hossain, A. M. Sadri, A. B. M. A. Al Islam

{"title":"DhakaNet: Unstructured Vehicle Detection using Limited Computational Resources","authors":"Tarik Reza Toha, Masfiqur Rahaman, Saiful Islam Salim, M. Hossain, A. M. Sadri, A. B. M. A. Al Islam","doi":"10.1109/ICDM51629.2021.00172","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00172","url":null,"abstract":"Inefficient traffic signal control system is one of the most important causes of traffic congestion in the cities of developing countries such as Bangladesh, India, Kenya, etc. This can be mitigated by adopting a decentralized traffic-responsive signal system, where vehicle detection is performed on the road through different image-based deep learning architectures amenable to limited-resource embedded platforms as available in developing countries. Deep learning architectures currently available in this regard demand high computational resources to achieve higher inference speed and better accuracy. Besides, the few existing limited-resource deep learning architectural alternatives neither attain higher inference speed nor substantial accuracy due to not overcoming the inherent limitations. To this extent, in this study, we propose a novel limited-resource deep learning architecture, namely DhakaNet, for real-time vehicle detection in on-road (street-view) traffic images. Our proposed architecture leverages enhancing Cross-Stage Partial Network and Path Aggregation Network to build the backbone and head networks, respectively. Besides, we develop a novel multi-scale attention module to extract multi-scale meaningful features from the images, where the developed multi-scale attention module boosts the detection accuracy at the cost of small overhead. Rigorous experimental evaluation of our proposed DhakaNet over three benchmark street-view traffic datasets such as DhakaAI, IITM-HeTra-A, and IITM-HeTra-B shows up to 51% faster inference speed at a similar accuracy, or up to 13% higher accuracy at a similar inference speed compared to other state-of-the-art limited-resource deep learning architectural alternatives.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128444468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning Dynamic User Interactions for Online Forum Commenting Prediction 学习动态用户交互用于在线论坛评论预测

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00168

Wu-Jiu Sun, X. Liu, Fei Shen

{"title":"Learning Dynamic User Interactions for Online Forum Commenting Prediction","authors":"Wu-Jiu Sun, X. Liu, Fei Shen","doi":"10.1109/ICDM51629.2021.00168","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00168","url":null,"abstract":"Predicting whether a user would interact with a particular message or topic in online social services is essential for applications related to recommendation systems. Two perspectives are commonly adopted when tackling this problem: the user’s interest in the post content and the social relationship between posters and commenters. However, explicit social relationships might not be available in online forums, e.g., Stack Overflow. This makes it challenging for predictive models to understand the social connections between forum users. In this paper, we propose a novel framework to solve the comment prediction problem in online forums. Specifically, the framework incorporates attention mechanisms to capture users’ interests in the post contents and a stacked graph convolutional network to perceive users’ implicit social relationships from their past temporal interactions. The multi-modal features learned by the framework would be combined together through a fusion layer and used for the final prediction. We verify the effectiveness of our framework from multiple perspectives using real forum datasets. Experimental results show that our framework could achieve better performance than existing state-of-the-art methods.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125250364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Robust Algorithm to Unifying Offline Causal Inference and Online Multi-armed Bandit Learning 一种统一离线因果推理和在线多臂强盗学习的鲁棒算法

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00071

Qiaoqiao Tang, Hong Xie

{"title":"A Robust Algorithm to Unifying Offline Causal Inference and Online Multi-armed Bandit Learning","authors":"Qiaoqiao Tang, Hong Xie","doi":"10.1109/ICDM51629.2021.00071","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00071","url":null,"abstract":"Utilizing offline logged data to improve sequential or online decision making is drawing more and more attention. VirUCB is one of the latest notable algorithmic framework in this research line, and it has both sound theoretical guarantee and nice empirical performance. However, regarding VirUCB, it is still unclear: (1) how imbalanced offline logged data influences the decision making accuracy; (2) how to schedule offline logged data across the decision making horizon so as to reduce offline logged data consumption. We show that with imbalanced offline logged data, VirUCB can have a learning speed slower than the baseline algorithm without offline logged data. This finding inspires us to design RobVirUCB algorithm, which is robust against such imbalanced data, i.e., still maintains a fast learning speed. RobVirUCB adaptively selects “useful” offline logged data to speed up learning and it has theoretical guarantees on regret. Finally, we design EffVirUCB algorithm, which reduces offline logged data consumption of RobVirUCB. EffVirUCB schedules the offline logged data to the decision round that the decision maker may select suboptimal arms and it has theoretical guarantees on regret. Extensive experiments on both synthetic data and real-world data validate the superior performance of RobVirUCB and EffVirUCB.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116795943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Preference-aware Group Task Assignment in Spatial Crowdsourcing: A Mutual Information-based Approach 空间众包中的偏好感知群体任务分配:一种基于互信息的方法

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00046

Yunchuan Li, Yan Zhao, Kai Zheng

{"title":"Preference-aware Group Task Assignment in Spatial Crowdsourcing: A Mutual Information-based Approach","authors":"Yunchuan Li, Yan Zhao, Kai Zheng","doi":"10.1109/ICDM51629.2021.00046","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00046","url":null,"abstract":"With the popularity of GPS-enable smart devices and the development of wireless network, Spatial Crowdsourcing (SC), as a framework for assigning location-sensitive tasks to moving workers, has received wide attention in recent years. In real-world scenarios, some complex tasks exist that may not be completed by a single worker. In this case, the tasks are often assigned to multiple workers, which is called group task assignment. However, the assignment of tasks that satisfy all group members in an even way remains a challenge. To this end, we propose a novel preference-aware group task assignment framework that includes two components: Mutual Information-based Preference Modeling (MIPM) and Preference-aware Group Task Assignment (PGTA). Specifically, MIPM learns the preferences of worker groups by maximizing the mutual information among workers based on the worker-task interaction data and the group-task interaction data, where an attention mechanism is used. PGTA adopts an optimal task assignment algorithm based on tree decomposition to assign tasks to appropriate worker groups, which aims to maximize the overall number of assigned tasks while giving priority to the groups of workers that are more interested in the tasks. Finally, extensive experiments are conducted, verifying the effectiveness and practicality of the proposed solutions.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117036353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

C3-GAN: Complex-Condition-Controlled Urban Traffic Estimation through Generative Adversarial Networks 基于生成对抗网络的复杂条件控制的城市交通估计

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00196

Yingxue Zhang, Yanhua Li, Xun Zhou, Zhenming Liu, Jun Luo

{"title":"C3-GAN: Complex-Condition-Controlled Urban Traffic Estimation through Generative Adversarial Networks","authors":"Yingxue Zhang, Yanhua Li, Xun Zhou, Zhenming Liu, Jun Luo","doi":"10.1109/ICDM51629.2021.00196","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00196","url":null,"abstract":"Given historical traffic distributions and associated urban conditions observed in a city, the conditional urban traffic estimation problem aims at estimating realistic future projections of the traffic under a set of new urban conditions, e.g., new bus routes, rainfall intensity and travel demands. The problem is important in reducing traffic congestion, improving public transportation efficiency, and facilitating urban planning. However, solving this problem is challenging due to the strong spatial dependencies of traffic patterns and the complex relations between the traffic and urban conditions. In this paper, we tackle the challenges by proposing a novel Complex-Condition-Controlled Urban Traffic Estimation through Generative Adversarial Networks (C3-GAN) for urban traffic estimation of a region under various complex conditions. C3-GAN features the following three novel designs on top of standard cGAN model: (1) an embedding network mapping the complex conditions to a latent space to find representations of the urban conditions; (2) an inference network to enhance the relations between the embedded latent vectors and the traffic data. Extensive experiments on real-world datasets demonstrate that our C3-GAN produces high-quality traffic estimations and outperforms state-of-the-art baseline methods.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121812992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2