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ORCIDPKDD / ECML 2020: Ghent, Belgium
- Yuxiao Dong, Georgiana Ifrim, Dunja Mladenic, Craig Saunders, Sofie Van Hoecke:
Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part V. Lecture Notes in Computer Science 12461, Springer 2021, ISBN 978-3-030-67669-8
Applied Data Science: Social Good
- Shammi More
, Simon B. Eickhoff, Julian Caspers, Kaustubh R. Patil:
Confound Removal and Normalization in Practice: A Neuroimaging Based Sex Prediction Case Study. 3-18 - Dilusha Weeraddana, Nguyen Lu Dang Khoa, Lachlan O'Neil, Weihong Wang, Chen Cai:
Energy Consumption Forecasting Using a Stacked Nonparametric Bayesian Approach. 19-35 - Keeyoung Kim, Jinseok Hong, Sang-Hoon Rhee, Simon S. Woo:
Reconstructing the Past: Applying Deep Learning to Reconstruct Pottery from Thousands Shards. 36-51 - Jiao Sun, Mingxuan Yue, Zongyu Lin, Xiaochen Yang, Luciano Nocera, Gabriel Kahn, Cyrus Shahabi:
CrimeForecaster: Crime Prediction by Exploiting the Geographical Neighborhoods' Spatiotemporal Dependencies. 52-67 - Ricky Maulana Fajri, Samaneh Khoshrou, Robert Peharz, Mykola Pechenizkiy:
PS3: Partition-Based Skew-Specialized Sampling for Batch Mode Active Learning in Imbalanced Text Data. 68-84 - Alexander Treiss, Jannis Walk, Niklas Kühl:
An Uncertainty-Based Human-in-the-Loop System for Industrial Tool Wear Analysis. 85-100 - Antoine Richard, Lior Fine, Offer Rozenstein, Josef Tanny, Matthieu Geist, Cédric Pradalier:
Filling Gaps in Micro-meteorological Data. 101-117 - Ferdinando Fioretto, Pascal Van Hentenryck, Terrence W. K. Mak, Cuong Tran, Federico Baldo, Michele Lombardi:
Lagrangian Duality for Constrained Deep Learning. 118-135
Applied Data Science: Healthcare
- Youssef Dawoud, Julia Hornauer, Gustavo Carneiro, Vasileios Belagiannis:
Few-Shot Microscopy Image Cell Segmentation. 139-154 - Pieter J. K. Libin, Arno Moonens, Timothy Verstraeten, Fabian Perez-Sanjines, Niel Hens, Philippe Lemey, Ann Nowé:
Deep Reinforcement Learning for Large-Scale Epidemic Control. 155-170 - Cristian Axenie, Daria Kurz:
GLUECK: Growth Pattern Learning for Unsupervised Extraction of Cancer Kinetics. 171-186 - Giuseppe Cannizzaro, Michele Leone, Anna Bernasconi, Arif Canakoglu, Mark J. Carman:
Automated Integration of Genomic Metadata with Sequence-to-Sequence Models. 187-203 - Derick M. de Oliveira, Antônio H. Ribeiro, João A. O. Pedrosa, Gabriela M. M. Paixão, Antônio Luiz P. Ribeiro, Wagner Meira Jr.:
Explaining End-to-End ECG Automated Diagnosis Using Contextual Features. 204-219
Applied Data Science: E-Commerce and Finance
- Siyu Lin, Peter A. Beling:
A Deep Reinforcement Learning Framework for Optimal Trade Execution. 223-240 - Filipe Lauar, Cristiano Arbex Valle:
Detecting and Predicting Evidences of Insider Trading in the Brazilian Market. 241-256 - Muhammad Umer Anwaar, Dmytro Rybalko, Martin Kleinsteuber:
Mend the Learning Approach, Not the Data: Insights for Ranking E-Commerce Products. 257-272 - Chin-Chia Michael Yeh, Zhongfang Zhuang, Wei Zhang, Liang Wang:
Multi-future Merchant Transaction Prediction. 273-289 - Karthik S. Gurumoorthy, Subhajit Sanyal, Vineet Chaoji:
Think Out of the Package: Recommending Package Types for E-Commerce Shipments. 290-305 - Ba-Hung Nguyen, Kiyoaki Shirai, Van-Nam Huynh:
Topics in Financial Filings and Bankruptcy Prediction with Distributed Representations of Textual Data. 306-322 - Yorick Spenrath, Marwan Hassani, Boudewijn F. van Dongen, Haseeb Tariq:
Why Did My Consumer Shop? Learning an Efficient Distance Metric for Retailer Transaction Data. 323-338 - Elaine M. Bettaney, Stephen R. Hardwick, Odysseas Zisimopoulos, Benjamin Paul Chamberlain:
Fashion Outfit Generation for E-Commerce. 339-354 - Supawit Vatathanavaro, Kitsuchart Pasupa, Sorratat Sirirattanajakarin, Boontawee Suntisrivaraporn:
Improved Identification of Imbalanced Multiple Annotation Intent Labels with a Hybrid BLSTM and CNN Model and Hybrid Loss Function. 355-368 - Riccardo Guidotti, Mirco Nanni, Fosca Giannotti, Dino Pedreschi, Simone Bertoli, Biagio Speciale, Hillel Rapoport:
Measuring Immigrants Adoption of Natives Shopping Consumption with Machine Learning. 369-385
Applied Data Science: Computational Social Science
- Keiichi Kisamori, Keisuke Yamazaki, Yuto Komori, Hiroshi Tokieda:
Model Bridging: Connection Between Simulation Model and Neural Network. 389-405 - Sara Abdali, Neil Shah, Evangelos E. Papalexakis:
Semi-supervised Multi-aspect Detection of Misinformation Using Hierarchical Joint Decomposition. 406-422 - Sai Saketh Aluru, Binny Mathew, Punyajoy Saha, Animesh Mukherjee:
A Deep Dive into Multilingual Hate Speech Classification. 423-439 - Qianru Wang, Bin Guo, Yi Ouyang, Kai Shu, Zhiwen Yu, Huan Liu:
Spatial Community-Informed Evolving Graphs for Demand Prediction. 440-456
Applied Data Science: Sports
- Tom Decroos, Maaike Van Roy, Jesse Davis:
SoccerMix: Representing Soccer Actions with Mixture Models. 459-474 - Danilo Sorano, Fabio Carrara, Paolo Cintia, Fabrizio Falchi, Luca Pappalardo:
Automatic Pass Annotation from Soccer Video Streams Based on Object Detection and LSTM. 475-490 - Javier Fernández, Luke Bornn:
SoccerMap: A Deep Learning Architecture for Visually-Interpretable Analysis in Soccer. 491-506 - Niander Assis, Renato M. Assunção, Pedro O. S. Vaz de Melo:
Stop the Clock: Are Timeout Effects Real? 507-523
Demo Track
- Eric Benhamou, David Saltiel, Jean-Jacques Ohana, Jamal Atif, Rida Laraki:
Deep Reinforcement Learning (DRL) for Portfolio Allocation. 527-531 - Christoffer Löffler, Christian Nickel, Christopher Sobel, Daniel Dzibela, Jonathan Braat, Benjamin Gruhler, Philipp Woller, Nicolas Witt, Christopher Mutschler:
Automated Quality Assurance for Hand-Held Tools via Embedded Classification and AutoML. 532-535 - Khadidja Meguelati, Benedicte Fontez, Nadine Hilgert, Florent Masseglia, Isabelle Sanchez:
Massively Distributed Clustering via Dirichlet Process Mixture. 536-540 - Qing Cui, Qitao Shi, Hao Qian, Caizhi Tang, Xixi Li, Yiming Zhao, Tao Jiang, Longfei Li, Jun Zhou:
AutoRec: A Comprehensive Platform for Building Effective and Explainable Recommender Models. 541-545 - Jacek Grycza, Damian Horna, Hanna Klimczak, Mateusz Lango, Kamil Plucinski, Jerzy Stefanowski:
multi-imbalance: Open Source Python Toolbox for Multi-class Imbalanced Classification. 546-549 - Clément Gautrais, Yann Dauxais, Samuel Kolb, Arcchit Jain, Mohit Kumar, Stefano Teso, Elia Van Wolputte, Gust Verbruggen, Luc De Raedt:
VisualSynth: Democratizing Data Science in Spreadsheets. 550-554 - Branislav Pecher, Ivan Srba, Róbert Móro, Matús Tomlein, Mária Bieliková:
FireAnt: Claim-Based Medical Misinformation Detection and Monitoring. 555-559 - Pieter Gijsbers, Joaquin Vanschoren:
GAMA: A General Automated Machine Learning Assistant. 560-564 - Kyungho Kim, Kyungjae Lee, Seung-won Hwang:
Instructional Video Summarization Using Attentive Knowledge Grounding. 565-569 - Christophe Couronne, Maksim Koptelov, Albrecht Zimmermann:
PrePeP: A Light-Weight, Extensible Tool for Predicting Frequent Hitters. 570-573
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