2021 IEEE International Conference on Data Mining (ICDM)最新文献_第8页

Matrix Profile XXIII: Contrast Profile: A Novel Time Series Primitive that Allows Real World Classification 矩阵剖面XXIII:对比剖面:一种允许真实世界分类的新型时间序列原语

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00151

nonymous” Ryan Mercer, S. Alaee, Alireza Abdoli, Shailendra Singh, Amy Murillo, Eamonn J. Keogh

引用次数: 8

SSDNet: State Space Decomposition Neural Network for Time Series Forecasting 用于时间序列预测的状态空间分解神经网络

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00048

Yang Lin, I. Koprinska, Mashud Rana

引用次数: 17

BaT: Beat-aligned Transformer for Electrocardiogram Classification 用于心电图分类的热对准变压器

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00043

Xiaoyu Li, Chen Li, Yuhua Wei, Yuyao Sun, Jishang Wei, Xiang Li, B. Qian

{"title":"BaT: Beat-aligned Transformer for Electrocardiogram Classification","authors":"Xiaoyu Li, Chen Li, Yuhua Wei, Yuyao Sun, Jishang Wei, Xiang Li, B. Qian","doi":"10.1109/ICDM51629.2021.00043","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00043","url":null,"abstract":"Electrocardiogram (ECG) is one of the critical diagnostic tools in healthcare. Various deep learning models, except Transformers, have been explored and applied to map ECG patterns to heart abnormalities. Transformer models have been adopted from natural language processing to computer vision with advanced features. Most recently, vision transformers show exceptional performances, even on moderate-scale datasets. However, naively applying vision transformers on electrocardiogram datasets leads to poor results. In this paper, we propose a novel network called Beat-aligned Transformer (BaT), a hierarchical Transformer that sufficiently exploits the cyclicity of ECG. We organize and treat an input ECG as multiple aligned beats instead of a single time series. In the BaT, shifted-window-based Transformer blocks (SW Block) are adopted to learn the representation for each beat, and aggregation blocks are designed to exchange information among the beat representations. Nested SW Blocks and aggregation blocks form a beat-aware hierarchical structure of BaT. In this way, the new data format and the BaT hierarchical structure boost Transformer performance on ECG classification. From the experiments on public ECG datasets, we observe BaT outperforms other Transformer-based models and achieves competitive performance compared with other state-of-the-art methods.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"1 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":"116340108","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: 8

Precise Bayes Classifier: Summary of Results 精确贝叶斯分类器:结果总结

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00076

Amin Vahedian, Xun Zhou

{"title":"Precise Bayes Classifier: Summary of Results","authors":"Amin Vahedian, Xun Zhou","doi":"10.1109/ICDM51629.2021.00076","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00076","url":null,"abstract":"The Bayes Classifier is shown to have the minimal classification error, in addition to interpretable predictions. However, it requires the knowledge of underlying distributions of the predictors to be usable. This requirement is almost never satisfied. Naive Bayes classifiers and variants estimate this classifier by assuming the independence among predictors. This restrictive assumption hinders both the accuracy of these classifiers and their interpretability, as the calculated probabilities become less reliable. Moreover, it is argued in the literature that interpretability comes at the expense of accuracy and vice versa. In this paper, we are motivated by the accurate and interpretable nature of the Bayes Classifier. We propose Precise Bayes, which is a computationally efficient estimation of the Bayes Classifier based on a new formulation. Our method makes no assumptions, neither on independence nor on underlying distributions. We devise a new theoretical minimal error rate for our formulation and show that the error rate of Precise Bayes approaches this limit with increasing number of samples learned. Moreover, the calculated posterior probabilities, are actual empirical probabilities calculated by counting the observations and outcomes. This makes the predictions made by Precise Bayes fully explainable. Our evaluations on generated datasets and real datasets validate our theoretical claims on prediction error rate and computational efficiency.","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":"126901349","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

Composition-Enhanced Graph Collaborative Filtering for Multi-behavior Recommendation 面向多行为推荐的组合增强图协同过滤

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00183

Daqing Wu, Xiao Luo, Zeyu Ma, Chong Chen, Pengfei Wang, Minghua Deng, Jinwen Ma

{"title":"Composition-Enhanced Graph Collaborative Filtering for Multi-behavior Recommendation","authors":"Daqing Wu, Xiao Luo, Zeyu Ma, Chong Chen, Pengfei Wang, Minghua Deng, Jinwen Ma","doi":"10.1109/ICDM51629.2021.00183","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00183","url":null,"abstract":"Rapid and accurate prediction of user preferences is the ultimate goal of today’s recommender systems. More and more researchers pay attention to multi-behavior recommender systems which utilize the auxiliary types of user-item interaction data, such as page view and add-to-cart to help estimate user preferences. Recently, graph-based methods were proposed to showcase an advanced capability in representation learning and capturing collaborative signals. However, we argue that these methods ignore the intrinsic difference between the two types of nodes in the bipartite graph and aggregate information from neighboring nodes with the same functions. Besides, these models do not fully explore the collaborative signals implied by the meta-path across different types of behavior, which causes a huge loss of the potential semantic information across behaviors. To address the above limitations, we present a unified graph model named SaGCN (short for Semantic-aware Graph Convolutional Networks). Specifically, we construct separate user-user and item-item graphs by meta-path, and apply separate aggregation and transformation functions to propagate user and item information. To perform better semantic propagation, we design a relation composition function and a semantic propagation architecture for heterogeneous collaborative filtering signals learning. Extensive experiments on two real-world datasets show that SaGCN outperforms a wide range of state-of-the-art methods in multi-behavior scenarios.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"36 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":"126679451","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: 3

PaGAN: Generative Adversarial Network for Patent understanding 专利理解的生成对抗网络

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00126

Guillaume Guarino, Ahmed Samet, Amir Nafi, D. Cavallucci

引用次数: 2

Summarizing User-Item Matrix By Group Utility Maximization 用群体效用最大化法总结用户-物品矩阵

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1145/3578586

Yongjie Wang, Ke Wang, Cheng Long, C. Miao

引用次数: 0

MetaGB: A Gradient Boosting Framework for Efficient Task Adaptive Meta Learning MetaGB:一个用于高效任务自适应元学习的梯度增强框架

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00020

Manqing Dong, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu

引用次数: 0

TRIO: Task-agnostic dataset representation optimized for automatic algorithm selection 为自动算法选择优化的任务不可知数据集表示

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00018

Noy Cohen-Shapira, L. Rokach

{"title":"TRIO: Task-agnostic dataset representation optimized for automatic algorithm selection","authors":"Noy Cohen-Shapira, L. Rokach","doi":"10.1109/ICDM51629.2021.00018","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00018","url":null,"abstract":"With the growing number of machine learning (ML) algorithms, the selection of the top-performing algorithms for a given dataset, task, and evaluation measure is known to be a challenging task. The human expertise required for this task has fueled the demand for automatic solutions. Meta-learning is a popular approach for automatic algorithm selection based on dataset characterization. Existing meta-learning methods often represent the datasets using predefined features and thus cannot be generalized for various ML tasks, or alternatively, learn their representations in a supervised fashion, and thus cannot address unsupervised tasks. In this study, we first propose a novel learning-based task-agnostic method for dataset representation. Second, we present TRIO, a meta-learning approach based on the proposed dataset representation, which is capable of accurately recommending top-performing algorithms for unseen datasets. TRIO first learns graphical representations from the datasets and then utilizes a graph convolutional neural network technique to extract their latent representations. An extensive evaluation on 337 datasets and 195 ML algorithms demonstrates the effectiveness of our approach over state-of-the-art methods for algorithm selection for both supervised (classification and regression) and unsupervised (clustering) tasks.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"9 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":"123930102","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: 3

MASCOT: A Quantization Framework for Efficient Matrix Factorization in Recommender Systems 推荐系统中高效矩阵分解的量化框架

2021 IEEE International Conference on Data Mining (ICDM) Pub Date : 2021-12-01 DOI: 10.1109/ICDM51629.2021.00039

Yunyong Ko, Jae-Seo Yu, Hong-Kyun Bae, Y. Park, Dongwon Lee, Sang-Wook Kim

{"title":"MASCOT: A Quantization Framework for Efficient Matrix Factorization in Recommender Systems","authors":"Yunyong Ko, Jae-Seo Yu, Hong-Kyun Bae, Y. Park, Dongwon Lee, Sang-Wook Kim","doi":"10.1109/ICDM51629.2021.00039","DOIUrl":"https://doi.org/10.1109/ICDM51629.2021.00039","url":null,"abstract":"In recent years, quantization methods have successfully accelerated the training of large deep neural network (DNN) models by reducing the level of precision in computing operations (e.g., forward/backward passes) without sacrificing its accuracy. In this work, therefore, we attempt to apply such a quantization idea to the popular Matrix factorization (MF) methods to deal with the growing scale of models and datasets in recommender systems. However, to our dismay, we observe that the state-of-the-art quantization methods are not effective in the training of MF models, unlike their successes in the training of DNN models. To this phenomenon, we posit that two distinctive features in training MF models could explain the difference: (i) the training of MF models is much more memory-intensive than that of DNN models, and (ii) the quantization errors across users and items in recommendation are not uniform. From these observations, we develop a quantization framework for MF models, named MASCOT, employing novel strategies (i.e., m-quantization and g-switching) to successfully address the aforementioned limitations of quantization in the training of MF models. The comprehensive evaluation using four real-world datasets demonstrates that MASCOT improves the training performance of MF models by about 45%, compared to the training without quantization, while maintaining low model errors, and the strategies and implementation optimizations of MASCOT are quite effective in the training of MF models. For the detailed information about MASCOT, we release the code of MASCOT and the datasets at: https://github.com/Yujaeseo/lCDM-2021_MASCOT.","PeriodicalId":320970,"journal":{"name":"2021 IEEE International Conference on Data Mining (ICDM)","volume":"165 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":"116395968","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: 7