2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)最新文献_第5页

Large-Scale Railway Networks Train Movements: A Dynamic, Interpretable, and Robust Hybrid Data Analytics System 大规模铁路网列车运行:一个动态、可解释和稳健的混合数据分析系统

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00048

Alessandro Lulli, L. Oneto, Renzo Canepa, Simone Petralli, D. Anguita

{"title":"Large-Scale Railway Networks Train Movements: A Dynamic, Interpretable, and Robust Hybrid Data Analytics System","authors":"Alessandro Lulli, L. Oneto, Renzo Canepa, Simone Petralli, D. Anguita","doi":"10.1109/DSAA.2018.00048","DOIUrl":"https://doi.org/10.1109/DSAA.2018.00048","url":null,"abstract":"We investigate the problem of analyzing the train movements in Large-Scale Railway Networks for the purpose of understanding and predicting their behaviour. We focus on different important aspects: the Running Time of a train between two stations, the Dwell Time of a train in a station, the Train Delay, and the Penalty Costs associated to a delay. Two main approaches exist in literature to study these aspects. One is based on the knowledge of the network and the experience of the operators. The other one is based on the analysis of the historical data about the network with advanced data analytics methods. In this paper, we will propose an hybrid approach in order to address the limitations of the current solutions. In fact, experience-based models are interpretable and robust but not really able to take into account all the factors which influence train movements resulting in low accuracy. From the other side, Data-Driven models are usually not easy to interpret, nor robust to infrequent events, and require a representative amount of data which is not always available if the phenomenon under examination changes too fast. Results on real world data coming from the Italian railway network will show that the proposed solution outperforms both state-of-the-art experience and Data-Driven based systems in terms of interpretability, robustness, ability to handle non recurrent events and changes in the behaviour of the network, and ability to consider complex and exogenous information.","PeriodicalId":208455,"journal":{"name":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133921684","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: 21

Kernel Regression on Manifold Valued Data 流形值数据的核回归

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00022

Alexander P. Kuleshov, A. Bernstein, Evgeny Burnaev

引用次数: 10

DSAA 2018 Organizing Committee DSAA 2018组委会

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/dsaa.2018.00007

引用次数: 0

Determinants of Growth in Micro and Small Enterprises: Empirical Evidence from Jordan 微型和小型企业成长的决定因素:来自约旦的经验证据

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00079

F. Awartani, B. Millis

引用次数: 4

Message from the DSAA 2018 General Co-Chairs 2018年DSAA联合主席致辞

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/dsaa.2018.00005

引用次数: 0

Message from the DSAA 2018 Program Co-Chairs 2018年DSAA项目联合主席致辞

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/dsaa.2018.00006

引用次数: 0

Neuro-Ensemble for Time Series Data Classification 时间序列数据分类的神经集成

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00015

S. F. Boubrahimi, Ruizhe Ma, R. Angryk

{"title":"Neuro-Ensemble for Time Series Data Classification","authors":"S. F. Boubrahimi, Ruizhe Ma, R. Angryk","doi":"10.1109/DSAA.2018.00015","DOIUrl":"https://doi.org/10.1109/DSAA.2018.00015","url":null,"abstract":"Combining a set of classification algorithms is a powerful technique in improving the accuracy of individual classifiers. There are two main paradigms in combining classifiers: classifier selection, where each classifier is considered as an expert in some local area of the feature space, and classifier fusion, where all classifiers are trained over the entire feature space and they are considered as competitive and complementary to each other. In this paper, we propose a new ensemble technique, NeuroEnsemble, that follows the classifier fusion paradigm applied on time series data. The Neuro-Ensemble exploits the idea that different classifiers participating in the ensemble have varying degrees of expertise on learning different class labels and it optimizes the ensemble using a shallow Multi-Layer Perceptron (MLP) based meta-learner to capture the expertise of individual classifiers. Every neuron in the MLP represents a classifier that contributes with a vote and performs activation and state computations. This work is the first attempt to train a neural network for learning the expertise of each classifier in an ensemble and optimize the entire classification schema based on class-level expertise weights. We validated our Neuro-Ensemble on 43 real-world time series datasets from the UCR repository. Our experimental results show the effectiveness and efficiency of our approach in comparison with individual baseline learners and ensemble techniques.","PeriodicalId":208455,"journal":{"name":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124488313","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

Mining Sensor Data for Predictive Maintenance in the Automotive Industry 面向汽车行业预测性维护的传感器数据挖掘

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00046

F. Giobergia, Elena Baralis, Maria Camuglia, T. Cerquitelli, M. Mellia, A. Neri, Davide Tricarico, A. Tuninetti

{"title":"Mining Sensor Data for Predictive Maintenance in the Automotive Industry","authors":"F. Giobergia, Elena Baralis, Maria Camuglia, T. Cerquitelli, M. Mellia, A. Neri, Davide Tricarico, A. Tuninetti","doi":"10.1109/DSAA.2018.00046","DOIUrl":"https://doi.org/10.1109/DSAA.2018.00046","url":null,"abstract":"Predictive maintenance is an ever-growing area of interest, spanning different fields and approaches. In the automotive industry faulty behaviors of the oxygen sensor are a key challenge to address. This paper presents OxyClog, a data-driven framework that, given a large number of time series collected from a vehicle's ECU (engine control unit), builds a model to predict if the oxygen sensor is currently unclogged, almost clogged (since the clogging of the sensor happens gradually), or clogged. OxyClog is characterized by a tailored preprocessing, which includes a custom and interpretable feature selection algorithm, along with a summarization strategy to transform a time-dependent problem into a time-independent one. Furthermore, a semi-supervised labeling methodology has been devised to use different data sources with different characteristics to define meaningful clogging labels. OxyClog integrates state-of-the-art classification algorithms – both interpretable and non-interpretable – to process real ECU data with good prediction performance.","PeriodicalId":208455,"journal":{"name":"2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128074892","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: 9

Automatic Semantic Labelling of Images by Their Content Using Non-Parametric Bayesian Machine Learning and Image Search Using Synthetically Generated Image Collages 使用非参数贝叶斯机器学习的图像内容自动语义标记和使用综合生成的图像拼贴的图像搜索

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00026

Michael Niemeyer, Ognjen Arandjelovic

引用次数: 8

Environmental and Geo-Spatial Data Analytics (EnGeoData'2018) 环境与地理空间数据分析(EnGeoData'2018)

2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2018-10-01 DOI: 10.1109/DSAA.2018.00073

M. Teisseire, M. Roche, D. Inkpen

引用次数: 0