2018 IEEE International Conference on Data Mining Workshops (ICDMW)最新文献_第6页

A Study on Customer Churn of Commercial Banks Based on Learning from Label Proportions 基于标签比例学习的商业银行客户流失研究

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00177

Yue Li, Bo Wang

{"title":"A Study on Customer Churn of Commercial Banks Based on Learning from Label Proportions","authors":"Yue Li, Bo Wang","doi":"10.1109/ICDMW.2018.00177","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00177","url":null,"abstract":"In this paper, we study the problem of predicting customer churn of commercial banks by using the historical transaction data of customers. In churn prediction, an important yet challenging problem is the enormous costs of labeling sample labels. In this paper, we use a learning method to predict customer churn, called learning from label proportions in which the training data is provided in groups and only the proportion of each class in each group is known. In other words, we use only the proportions of the churners in each group to predict the latent customer churn. The proportion information in each group can be estimated by the experience of the commercial bank's customer manager, so it can greatly reduce the costs caused by labeling sample churn labels. In the experimental section, we construct the problems of supervised learning and learning from label proportions respectively. We adopt Logistic Regression (LR) and Support Vector Machine (SVM) for supervised learning and proportion-SVM (Alter - ∝SVM) for learning from label proportions respectively. In addition, we use genetic algorithm to solve the non-convex integer programming problem in the problem of learning from label proportions.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116951231","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

On Using Clustering for the Optimization of Hydrological Simulations 基于聚类的水文模拟优化研究

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00215

E. Azmi

引用次数: 1

AutoClustering: A Feed-Forward Neural Network Based Clustering Algorithm 自动聚类:一种前馈神经网络聚类算法

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00102

M. Kimura

引用次数: 4

Detecting Outliers in Streaming Time Series Data from ARM Distributed Sensors ARM分布式传感器流时间序列数据异常点检测

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00117

Yuping Lu, J. Kumar, N. Collier, Bhargavi Krishna, M. Langston

{"title":"Detecting Outliers in Streaming Time Series Data from ARM Distributed Sensors","authors":"Yuping Lu, J. Kumar, N. Collier, Bhargavi Krishna, M. Langston","doi":"10.1109/ICDMW.2018.00117","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00117","url":null,"abstract":"The Atmospheric Radiation Measurement (ARM) Data Center at ORNL collects data from a number of permanent and mobile facilities around the globe. The data is then ingested to create high level scientific products. High frequency streaming measurements from sensors and radar instruments at ARM sites require high degree of accuracy to enable rigorous study of atmospheric processes. Outliers in collected data are common due to instrument failure or extreme weather events. Thus, it is critical to identify and flag them. We employed multiple univariate, multivariate and time series techniques for outlier detection methods and studied their effectiveness. First, we examined Pearson correlation coefficient which is used to measure the pairwise correlations between variables. Singular Spectrum Analysis (SSA) was applied to detect outliers by removing the anticipated annual and seasonal cycles from the signal to accentuate anomalies. K-means was applied for multivariate examination of data from collection of sensors to identify any deviation from expected and known patterns and identify abnormal observation. The Pearson correlation coefficient, SSA and K-means methods were later combined together in a framework to detect outliers through a range of checks. We applied the developed method to data from meteorological sensors at ARM Southern Great Plains site and validated against existing database of known data quality issues.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116284964","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

Multidimensional Data Mining Based on Tensor 基于张量的多维数据挖掘

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00164

Ryohei Yokobayashi, T. Miura

引用次数: 1

Road Speed Profiling for Upfront Travel Time Estimation 道路速度分析的前期旅行时间估计

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00100

Abhinav Sunderrajan, Jagannadan Varadarajan, Kong-wei Lye

引用次数: 3

Joint Classification Model of Topic and Polarity: Finding Satisfaction and Dissatisfaction Factors from Airport Service Review 主题与极性联合分类模型:从机场服务评价中寻找满意与不满意因素

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00126

Kosuke Mizufune, Sotaro Katsumata

引用次数: 5

Collaborative Anomaly Detection on Blockchain from Noisy Sensor Data 基于噪声传感器数据的区块链协同异常检测

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00024

T. Idé

引用次数: 21

On the Sustainability of Blockchain Funding 关于区块链融资的可持续性

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00020

Bingsheng Zhang, Hamed Balogun

{"title":"On the Sustainability of Blockchain Funding","authors":"Bingsheng Zhang, Hamed Balogun","doi":"10.1109/ICDMW.2018.00020","DOIUrl":"https://doi.org/10.1109/ICDMW.2018.00020","url":null,"abstract":"Blockchain technology has pioneered a new consensus approach to building a distributed public ledger globally. A key feature expected from cryptocurrencies and blockchain systems is the absence of a centralized control over the operation process. That is, blockchain solutions should neither rely on \"trusted parties or powerful minority\" for their operations nor introduce such centralisation tendencies into blockchain systems. On the other hand, real-world blockchain systems require steady funding for continuous development and maintenance of the systems. Given that blockchain systems are decentralized systems, their maintenance and developmental funding should also be void of centralization risks. Therefore, secure and \"community-inclusive\" long-term sustainability of funding is critical for the health of blockchain platforms. In this work, for the first time, we provide a systematic exposition of blockchain development funding, planning, manage-ment, and disbursement mechanisms aka \"treasury systems\" (for cryptocurrencies and blockchain systems). Drawing from existing literature, we identify and categorise various treasury models, thereby enabling an exploration of their properties, benefits and drawbacks. Particularly, we perform an evaluation of real-world cryptocurrency treasury system of top cryptocurrencies e.g., Dash governance system, and ZCash Foundation. Finally, we briefly discuss desired properties of decentralised treasury systems and provide suggestions for improvement or alternative solutions to existing systems or implementations.","PeriodicalId":259600,"journal":{"name":"2018 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124394290","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

Attend2trend: Attention Model for Real-Time Detecting and Forecasting of Trending Topics 趋势:趋势话题实时检测和预测的注意力模型

2018 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2018-11-01 DOI: 10.1109/ICDMW.2018.00222

Ahmed Saleh, A. Scherp

引用次数: 0