2017 IEEE International Conference on Data Mining Workshops (ICDMW)最新文献_第7页

IP2Vec: Learning Similarities Between IP Addresses IP2Vec:学习IP地址之间的相似性

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.93

Markus Ring, Alexander Dallmann, D. Landes, A. Hotho

引用次数: 50

Exploring Uncertainty Methods for Centrality Analysis in Social Networks 探索社会网络中中心性分析的不确定性方法

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.27

Xianglin Zuo, Bo Yang, Wanli Zuo

引用次数: 2

Semi-Supervised Prediction of Comorbid Rare Conditions Using Medical Claims Data 利用医疗索赔数据的半监督预测共病罕见病

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.68

Chirag Nagpal, K. Miller, Tiffany Pellathy, M. Hravnak, G. Clermont, M. Pinsky, A. Dubrawski

{"title":"Semi-Supervised Prediction of Comorbid Rare Conditions Using Medical Claims Data","authors":"Chirag Nagpal, K. Miller, Tiffany Pellathy, M. Hravnak, G. Clermont, M. Pinsky, A. Dubrawski","doi":"10.1109/ICDMW.2017.68","DOIUrl":"https://doi.org/10.1109/ICDMW.2017.68","url":null,"abstract":"Medical insurance claims data offer a coarse view of a patient's medical profile, including information about previous diagnoses and procedures performed. These data have been exploited in the past to predict presence of unmanifested conditions. Rarer conditions however, provide an extremely limited amount of ground truth to train supervised models, but predicting relevant co-morbidities can help reduce failure to rescue from a treatable, yet potentially life threatening condition. In this paper, we aim at a formidable task of improving models built to predict comorbidity of rare conditions that emerge during hospitalization and present PreCoRC, a novel approach that leverages hierarchical structures of diagnosis and procedure codes to alleviate the relatively low prevalence of specific types of Failure to Rescue (FTR) incidents. It can be applied post-hoc over previously learnt predictive models, and used to discover parts of the underlying hierarchies that contribute to the task. Our experimental results demonstrate that PreCoRC carries promise for operational utility in clinical settings, and offer insights into potential leading indicators of life threatening complications.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"106 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127897245","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

Failure Prediction with Adaptive Multi-scale Sampling and Activation Pattern Regularization 基于自适应多尺度采样和激活模式正则化的故障预测

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.17

Yujin Tang, Shinya Wada, K. Yoshihara

{"title":"Failure Prediction with Adaptive Multi-scale Sampling and Activation Pattern Regularization","authors":"Yujin Tang, Shinya Wada, K. Yoshihara","doi":"10.1109/ICDMW.2017.17","DOIUrl":"https://doi.org/10.1109/ICDMW.2017.17","url":null,"abstract":"We treat failure prediction in a supervised learning framework using a convolutional neural network (CNN). Due to the nature of the problem, learning a CNN model on this kind of dataset is generally associated with three primary problems: 1) negative samples (indicating a healthy system) outnumber positives (indicating system failures) by a great margin; 2) implementation design often requires chopping an original time series into sub-sequences, defining a segmentation window size with sufficient data augmentation and avoiding serious multiple-instance learning issue is non-trivial; 3) positive samples may have a common underlying cause and thus present similar features, negative samples can have various latent characteristics which can \"distract\" CNN in the learning process. While the first problem has been extensively discussed in literatures, the last two issues are less explored in the context of deep learning using CNN. We mitigate the second problem by introducing a random variable on sample scaling parameters, whose distribution's parameters are jointly learnt with CNN and leads to what we call adaptive multi-scale sampling (AMS). To address the third problem, we propose activation pattern regularization (APR) on only positive samples such that the CNN focuses on learning representations pertaining to the underlying common cause. We demonstrate the effectiveness of our proposals on a past Kaggle contest dataset that predicts seizures from EEG data. Compared to the baseline method with a CNN trained in traditional scheme, we observe significant performance improvement for both proposed methods. When combined, our model without any sophisticated hyper-parameter tuning or ensemble methods shows a near 10% relative improvement on AUROC and is able to send us to the 14th place on the contest's leaderboard while the highest rank the baseline can reach is 77th.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122524257","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

Semantic Visualization Support for Innovators Marketplace on Data Jackets 数据夹克上创新者市场的语义可视化支持

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.85

Qi Wang

引用次数: 0

Dataset Selection for Controlling Swarms by Visual Demonstration 蜂群控制的可视化演示数据集选择

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.128

K. K. Budhraja, T. Oates

{"title":"Dataset Selection for Controlling Swarms by Visual Demonstration","authors":"K. K. Budhraja, T. Oates","doi":"10.1109/ICDMW.2017.128","DOIUrl":"https://doi.org/10.1109/ICDMW.2017.128","url":null,"abstract":"Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. Without the involvement of manual behavior specification via code or reliance on a defined taxonomy of possible behaviors, the demonstrator specifies spatial motion of the agents over time, and retrieves agent-level parameters required to execute that motion. A framework for reproducing emergent behavior, given an abstract demonstration, is discussed in existing work. Our work extends that framework by addressing the variation in reproduced behavior over several executions of the framework. The cause for such variation is identified to be the capacity of training data to represent the demonstration. Addressing this problem produces more favorable (more similar to the demonstration) replicated emergent behaviors. Our work is evaluated using demonstrations and visual features as in the aforementioned work. Experimental results show an improvement in the coherence between demonstrated behavior, and the corresponding replicated behavior produced by the framework.","PeriodicalId":389183,"journal":{"name":"2017 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115290360","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

Distributed Representations of Subgraphs 子图的分布式表示

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.20

B. Adhikari, Yao Zhang, Naren Ramakrishnan, B. Prakash

引用次数: 22

Apollo: Near-Duplicate Detection for Job Ads in the Online Recruitment Domain 阿波罗:在线招聘领域招聘广告的近重复检测

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.29

Hunter Burk, F. Javed, Janani Balaji

引用次数: 4

Extracting Field Oversees’ Features in Risk Recognition from Data of Eyes and Utterances 从眼睛和话语数据中提取风险识别中的场监督特征

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.83

N. Kushiro, Yuji Fujita, Yusuke Aoyama

引用次数: 5

Discovery of Action Rules at Lowest Cost in Spark 在Spark中以最低成本发现动作规则

2017 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2017-11-01 DOI: 10.1109/ICDMW.2017.173

A. Tzacheva, A. Bagavathi, Lavanya Ayila

引用次数: 9